{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook I will explore the data and ascertain their structure. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Functions](#Functions)\n",
    "\n",
    "2. [Data](#Data)\n",
    "\n",
    "2. [NaN values](#NaN values)\n",
    "\n",
    "2. [Convert date column to datetime format](#Convert date column to datetime format)\n",
    "\n",
    "2. [Impute missing values](#Impute missing values)\n",
    "\n",
    "2. [General comments on the features](#General comments on the features)\n",
    "\n",
    "2. [A closer look](#A closer look)\n",
    "\n",
    "2. [Locations](#Locations)\n",
    "\n",
    "2. [Format of the data](#Format of the data)\n",
    "\n",
    "2. [Precipitation](#Precipitation)\n",
    "\n",
    "2. [Temperature](#Temperature)\n",
    "\n",
    "2. [Time series](#Time series)\n",
    "\n",
    "2. [Basic statistics](#Basic statistics)\n",
    "\n",
    "2. [Correlations](#Correlations)\n",
    "\n",
    "2. [Drop features/columns](#Drop features/columns)\n",
    "\n",
    "2. [Save cleaned data to disk](#Save cleaned data to disk)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** For the internal links in the table of contents to work, please open the notebook in the Jupyter notebook viewer <a href=\"http://nbviewer.jupyter.org/github/cleipski/CropPredict/blob/master/01_data_exploration.ipynb\" target=\"_blank\">here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Functions'></a>\n",
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate distance given long/lat\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "\n",
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "    \"\"\"\n",
    "    Calculate the great circle distance between two points \n",
    "    on the earth (specified in decimal degrees)\n",
    "    \"\"\"\n",
    "    # convert decimal degrees to radians \n",
    "    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n",
    "    # haversine formula \n",
    "    dlon = lon2 - lon1 \n",
    "    dlat = lat2 - lat1 \n",
    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "    c = 2. * asin(sqrt(a)) \n",
    "    r = 6371. # Radius of earth in kilometers. Use 3956 for miles\n",
    "    return c * r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_with_nearest(in_df,in_column):\n",
    "    \"\"\"\n",
    "    Replace missing values in 'in_column' of the data frame 'in_df' \n",
    "    with values from the same day that are closest geographically. \n",
    "    \"\"\"\n",
    "    # Data frame of all the 'NaN's    \n",
    "    df_nan = in_df[in_df[in_column].isnull()]\n",
    "    if len(df_nan) > 0:\n",
    "        # Same date, similar locations\n",
    "        for i in range(len(df_nan)):\n",
    "            tmp_date = df_nan['Date'].iloc[i]\n",
    "            tmp_long = df_nan['Longitude'].iloc[i]\n",
    "            tmp_lat  = df_nan['Latitude'].iloc[i]\n",
    "            tmp_index = df_nan['Latitude'].index.tolist()[i]\n",
    "            condition = (((in_df['Latitude'] != tmp_lat) |\n",
    "                         (in_df['Longitude'] != tmp_long)) & \n",
    "                         (in_df['Date'] == tmp_date) & \n",
    "                         (in_df[in_column].notnull())) \n",
    "            # Create temporary data frame for convenient ordering\n",
    "            new_df = in_df[['Longitude', 'Latitude']][condition]\n",
    "            new_df['Distance'] = new_df['Longitude']\n",
    "            for j in range(len(new_df)):\n",
    "                new_df['Distance'].iloc[j] = haversine(tmp_long,\n",
    "                                                       tmp_lat,\n",
    "                                                       new_df['Longitude'].iloc[j],\n",
    "                                                       new_df['Latitude'].iloc[j])\n",
    "            # Distance to nearest measurement on same day\n",
    "            tmp_df = new_df.sort_values(by='Distance', ascending=True)\n",
    "            tmp_min_dist = tmp_df.iloc[0,2]\n",
    "            tmp_min_index = tmp_df.index.tolist()[0]\n",
    "            # Replace\n",
    "            old_val = in_df[in_column].iloc[tmp_index]\n",
    "            new_val = in_df[in_column].iloc[tmp_min_index]\n",
    "            in_df[in_column].iloc[tmp_index] = new_val\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data'></a>\n",
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountyName</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>precipTypeIsOther</th>\n",
       "      <th>pressure</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>visibility</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>windSpeed</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>DayInSeason</th>\n",
       "      <th>Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>46.811686</td>\n",
       "      <td>-118.695237</td>\n",
       "      <td>11/30/2013 0:00</td>\n",
       "      <td>35.70</td>\n",
       "      <td>20.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1027.13</td>\n",
       "      <td>35.70</td>\n",
       "      <td>27.48</td>\n",
       "      <td>2.46</td>\n",
       "      <td>214</td>\n",
       "      <td>1.18</td>\n",
       "      <td>134.110657</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>46.929839</td>\n",
       "      <td>-118.352109</td>\n",
       "      <td>11/30/2013 0:00</td>\n",
       "      <td>35.10</td>\n",
       "      <td>26.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.77</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1026.87</td>\n",
       "      <td>35.10</td>\n",
       "      <td>26.92</td>\n",
       "      <td>2.83</td>\n",
       "      <td>166</td>\n",
       "      <td>1.01</td>\n",
       "      <td>131.506592</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.006888</td>\n",
       "      <td>-118.510160</td>\n",
       "      <td>11/30/2013 0:00</td>\n",
       "      <td>33.38</td>\n",
       "      <td>26.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.36</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1026.88</td>\n",
       "      <td>33.38</td>\n",
       "      <td>26.95</td>\n",
       "      <td>2.95</td>\n",
       "      <td>158</td>\n",
       "      <td>1.03</td>\n",
       "      <td>131.472946</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.162342</td>\n",
       "      <td>-118.699677</td>\n",
       "      <td>11/30/2013 0:00</td>\n",
       "      <td>28.05</td>\n",
       "      <td>25.93</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.47</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1026.37</td>\n",
       "      <td>33.19</td>\n",
       "      <td>27.17</td>\n",
       "      <td>2.89</td>\n",
       "      <td>153</td>\n",
       "      <td>1.84</td>\n",
       "      <td>131.288300</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.157512</td>\n",
       "      <td>-118.434056</td>\n",
       "      <td>11/30/2013 0:00</td>\n",
       "      <td>28.83</td>\n",
       "      <td>25.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1026.19</td>\n",
       "      <td>33.85</td>\n",
       "      <td>27.07</td>\n",
       "      <td>2.97</td>\n",
       "      <td>156</td>\n",
       "      <td>1.85</td>\n",
       "      <td>131.288300</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountyName       State   Latitude   Longitude             Date  \\\n",
       "0      Adams  Washington  46.811686 -118.695237  11/30/2013 0:00   \n",
       "1      Adams  Washington  46.929839 -118.352109  11/30/2013 0:00   \n",
       "2      Adams  Washington  47.006888 -118.510160  11/30/2013 0:00   \n",
       "3      Adams  Washington  47.162342 -118.699677  11/30/2013 0:00   \n",
       "4      Adams  Washington  47.157512 -118.434056  11/30/2013 0:00   \n",
       "\n",
       "   apparentTemperatureMax  apparentTemperatureMin  cloudCover  dewPoint  \\\n",
       "0                   35.70                   20.85        0.00     29.53   \n",
       "1                   35.10                   26.92        0.00     29.77   \n",
       "2                   33.38                   26.95        0.00     29.36   \n",
       "3                   28.05                   25.93        0.91     29.47   \n",
       "4                   28.83                   25.98        0.91     29.86   \n",
       "\n",
       "   humidity  ...    precipTypeIsOther  pressure  temperatureMax  \\\n",
       "0      0.91  ...                    0   1027.13           35.70   \n",
       "1      0.93  ...                    0   1026.87           35.10   \n",
       "2      0.94  ...                    0   1026.88           33.38   \n",
       "3      0.94  ...                    0   1026.37           33.19   \n",
       "4      0.94  ...                    0   1026.19           33.85   \n",
       "\n",
       "   temperatureMin  visibility  windBearing  windSpeed        NDVI  \\\n",
       "0           27.48        2.46          214       1.18  134.110657   \n",
       "1           26.92        2.83          166       1.01  131.506592   \n",
       "2           26.95        2.95          158       1.03  131.472946   \n",
       "3           27.17        2.89          153       1.84  131.288300   \n",
       "4           27.07        2.97          156       1.85  131.288300   \n",
       "\n",
       "   DayInSeason  Yield  \n",
       "0            0   35.7  \n",
       "1            0   35.7  \n",
       "2            0   35.7  \n",
       "3            0   35.7  \n",
       "4            0   35.7  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwd = os.getcwd()\n",
    "data = os.path.join(cwd,'data','wheat-2013-supervised.csv')\n",
    "df_2013 = pd.read_csv(data)\n",
    "df_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>CountyName</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>precipTypeIsOther</th>\n",
       "      <th>pressure</th>\n",
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       "      <th>temperatureMin</th>\n",
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       "      <th>windBearing</th>\n",
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       "      <th>NDVI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Washington</td>\n",
       "      <td>47.150327</td>\n",
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       "      <td>11/30/2014 0:00</td>\n",
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       "      <td>135.676956</td>\n",
       "      <td>0</td>\n",
       "      <td>35.6</td>\n",
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       "      <th>3</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.162342</td>\n",
       "      <td>-118.699677</td>\n",
       "      <td>11/30/2014 0:00</td>\n",
       "      <td>19.69</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>0.03</td>\n",
       "      <td>7.32</td>\n",
       "      <td>0.69</td>\n",
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       "      <td>1027.74</td>\n",
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       "      <td>8.10</td>\n",
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       "      <td>1</td>\n",
       "      <td>5.18</td>\n",
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       "      <td>0</td>\n",
       "      <td>35.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.157512</td>\n",
       "      <td>-118.434056</td>\n",
       "      <td>11/30/2014 0:00</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1027.16</td>\n",
       "      <td>24.83</td>\n",
       "      <td>8.32</td>\n",
       "      <td>9.99</td>\n",
       "      <td>5</td>\n",
       "      <td>4.69</td>\n",
       "      <td>134.803864</td>\n",
       "      <td>0</td>\n",
       "      <td>35.6</td>\n",
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       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountyName       State   Latitude   Longitude             Date  \\\n",
       "0      Adams  Washington  46.929839 -118.352109  11/30/2014 0:00   \n",
       "1      Adams  Washington  47.150327 -118.958859  11/30/2014 0:00   \n",
       "2      Adams  Washington  46.811686 -118.695237  11/30/2014 0:00   \n",
       "3      Adams  Washington  47.162342 -118.699677  11/30/2014 0:00   \n",
       "4      Adams  Washington  47.157512 -118.434056  11/30/2014 0:00   \n",
       "\n",
       "   apparentTemperatureMax  apparentTemperatureMin  cloudCover  dewPoint  \\\n",
       "0                   18.61                   -3.01        0.00      6.77   \n",
       "1                   19.67                   -0.74        0.00      6.66   \n",
       "2                   20.66                   -0.14        0.00      6.55   \n",
       "3                   19.69                   -2.66        0.03      7.32   \n",
       "4                   18.82                   -3.04        0.04      7.62   \n",
       "\n",
       "   humidity  ...    precipTypeIsOther  pressure  temperatureMax  \\\n",
       "0      0.69  ...                    0   1027.95           23.93   \n",
       "1      0.65  ...                    0   1028.26           25.88   \n",
       "2      0.67  ...                    0   1028.29           24.67   \n",
       "3      0.69  ...                    0   1027.74           25.48   \n",
       "4      0.70  ...                    0   1027.16           24.83   \n",
       "\n",
       "   temperatureMin  visibility  windBearing  windSpeed        NDVI  \\\n",
       "0            6.96       10.00            9       3.80  136.179718   \n",
       "1            8.71       10.00          352       6.03  135.697540   \n",
       "2            8.26       10.00           25       3.59  135.676956   \n",
       "3            8.10       10.00            1       5.18  135.005798   \n",
       "4            8.32        9.99            5       4.69  134.803864   \n",
       "\n",
       "   DayInSeason  Yield  \n",
       "0            0   35.6  \n",
       "1            0   35.6  \n",
       "2            0   35.6  \n",
       "3            0   35.6  \n",
       "4            0   35.6  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also read data for 2014\n",
    "data = os.path.join(cwd,'data','wheat-2014-supervised.csv')\n",
    "df_2014 = pd.read_csv(data)\n",
    "df_2014.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='NaN values'></a>\n",
    "## NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 177493 entries, 0 to 177492\n",
      "Data columns (total 26 columns):\n",
      "CountyName                177493 non-null object\n",
      "State                     177493 non-null object\n",
      "Latitude                  177493 non-null float64\n",
      "Longitude                 177493 non-null float64\n",
      "Date                      177493 non-null object\n",
      "apparentTemperatureMax    177493 non-null float64\n",
      "apparentTemperatureMin    177493 non-null float64\n",
      "cloudCover                177493 non-null float64\n",
      "dewPoint                  177493 non-null float64\n",
      "humidity                  177493 non-null float64\n",
      "precipIntensity           177492 non-null float64\n",
      "precipIntensityMax        177492 non-null float64\n",
      "precipProbability         177492 non-null float64\n",
      "precipAccumulation        177493 non-null float64\n",
      "precipTypeIsRain          177493 non-null int64\n",
      "precipTypeIsSnow          177493 non-null int64\n",
      "precipTypeIsOther         177493 non-null int64\n",
      "pressure                  177239 non-null float64\n",
      "temperatureMax            177493 non-null float64\n",
      "temperatureMin            177493 non-null float64\n",
      "visibility                177463 non-null float64\n",
      "windBearing               177493 non-null int64\n",
      "windSpeed                 177493 non-null float64\n",
      "NDVI                      177493 non-null float64\n",
      "DayInSeason               177493 non-null int64\n",
      "Yield                     177493 non-null float64\n",
      "dtypes: float64(18), int64(5), object(3)\n",
      "memory usage: 35.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the general properties of the DFs\n",
    "df_2013.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 182549 entries, 0 to 182548\n",
      "Data columns (total 26 columns):\n",
      "CountyName                182549 non-null object\n",
      "State                     182549 non-null object\n",
      "Latitude                  182549 non-null float64\n",
      "Longitude                 182549 non-null float64\n",
      "Date                      182549 non-null object\n",
      "apparentTemperatureMax    182549 non-null float64\n",
      "apparentTemperatureMin    182549 non-null float64\n",
      "cloudCover                182549 non-null float64\n",
      "dewPoint                  182549 non-null float64\n",
      "humidity                  182549 non-null float64\n",
      "precipIntensity           182549 non-null float64\n",
      "precipIntensityMax        182549 non-null float64\n",
      "precipProbability         182549 non-null float64\n",
      "precipAccumulation        182549 non-null float64\n",
      "precipTypeIsRain          182549 non-null int64\n",
      "precipTypeIsSnow          182549 non-null int64\n",
      "precipTypeIsOther         182549 non-null int64\n",
      "pressure                  182198 non-null float64\n",
      "temperatureMax            182549 non-null float64\n",
      "temperatureMin            182549 non-null float64\n",
      "visibility                182533 non-null float64\n",
      "windBearing               182549 non-null int64\n",
      "windSpeed                 182549 non-null float64\n",
      "NDVI                      182549 non-null float64\n",
      "DayInSeason               182549 non-null int64\n",
      "Yield                     182549 non-null float64\n",
      "dtypes: float64(18), int64(5), object(3)\n",
      "memory usage: 36.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_2014.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both years I have 26 columns with ~177k rows for 2013 and ~183k rows for 2014. All columns show the expected format for the data they contain (float, integer, string). However, for each year a number of columns have a few entries that are 'NaN' (compare number on non-null entries with total number of rows)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountyName                  0\n",
       "State                       0\n",
       "Latitude                    0\n",
       "Longitude                   0\n",
       "Date                        0\n",
       "apparentTemperatureMax      0\n",
       "apparentTemperatureMin      0\n",
       "cloudCover                  0\n",
       "dewPoint                    0\n",
       "humidity                    0\n",
       "precipIntensity             1\n",
       "precipIntensityMax          1\n",
       "precipProbability           1\n",
       "precipAccumulation          0\n",
       "precipTypeIsRain            0\n",
       "precipTypeIsSnow            0\n",
       "precipTypeIsOther           0\n",
       "pressure                  254\n",
       "temperatureMax              0\n",
       "temperatureMin              0\n",
       "visibility                 30\n",
       "windBearing                 0\n",
       "windSpeed                   0\n",
       "NDVI                        0\n",
       "DayInSeason                 0\n",
       "Yield                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's verify\n",
    "df_2013.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountyName                  0\n",
       "State                       0\n",
       "Latitude                    0\n",
       "Longitude                   0\n",
       "Date                        0\n",
       "apparentTemperatureMax      0\n",
       "apparentTemperatureMin      0\n",
       "cloudCover                  0\n",
       "dewPoint                    0\n",
       "humidity                    0\n",
       "precipIntensity             0\n",
       "precipIntensityMax          0\n",
       "precipProbability           0\n",
       "precipAccumulation          0\n",
       "precipTypeIsRain            0\n",
       "precipTypeIsSnow            0\n",
       "precipTypeIsOther           0\n",
       "pressure                  351\n",
       "temperatureMax              0\n",
       "temperatureMin              0\n",
       "visibility                 16\n",
       "windBearing                 0\n",
       "windSpeed                   0\n",
       "NDVI                        0\n",
       "DayInSeason                 0\n",
       "Yield                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2014.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2013, five features have 'NaN' values, in 2014 only 2 features. The feature with the highest number of 'NaNs' is 'pressure' followed by 'visibilty' for both years. All missing values are from features that are weather related."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Convert date column to datetime format'></a>\n",
    "## Convert date column to datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exec. time: 90.12 s\n"
     ]
    }
   ],
   "source": [
    "# Check if already converted b/c it takes a long time to process\n",
    "now = time.time()\n",
    "if not df_2013['Date'].dtype == np.dtype('datetime64[ns]'):\n",
    "    df_2013['Date'] = df_2013['Date'].apply(pd.to_datetime)\n",
    "print('Exec. time: {:5.2f} s'.format(time.time()-now))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exec. time: 96.87 s\n"
     ]
    }
   ],
   "source": [
    "# Check if already converted b/c it takes a long time to process\n",
    "now = time.time()\n",
    "if not df_2014['Date'].dtype == np.dtype('datetime64[ns]'):\n",
    "    df_2014['Date'] = df_2014['Date'].apply(pd.to_datetime)\n",
    "print('Exec. time: {:5.2f} s'.format(time.time()-now))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Impute missing values'></a>\n",
    "## Impute missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Because the data are collected in very different areas, a global average can not be used for the imputed value (which are weather data, i.e. local). Also, averages over time at one location makes not much sense because the weather is likely to change over time. Therefore, I use data from the same day and from the closest neighboring location to replace the 'NaN' values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see missing values in the following fields:\n",
    "\n",
    "* 2013: precipIntensity, precipIntensityMax, precipProbability, pressure, visibility\n",
    "* 2014: pressure, visibility     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exec. time: 91.58 s\n",
      "Exec. time: 114.74 s\n"
     ]
    }
   ],
   "source": [
    "now = time.time()\n",
    "impute_with_nearest(df_2013, 'precipIntensity')\n",
    "impute_with_nearest(df_2013, 'precipIntensityMax')\n",
    "impute_with_nearest(df_2013, 'precipProbability')\n",
    "impute_with_nearest(df_2013, 'pressure')\n",
    "impute_with_nearest(df_2013, 'visibility')\n",
    "print('Exec. time: {:5.2f} s'.format(time.time()-now))\n",
    "# #\n",
    "now = time.time()\n",
    "impute_with_nearest(df_2014, 'pressure')\n",
    "impute_with_nearest(df_2014, 'visibility')\n",
    "print('Exec. time: {:5.2f} s'.format(time.time()-now))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Check result\n",
    "print(df_2013.isnull().sum().sum())\n",
    "print(df_2014.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='General comments on the features'></a>\n",
    "## General comments on the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of features/columns in the dataset where it is not readily apparent what the encode or what their units are. Common definitions are:\n",
    "\n",
    "* precipIntensity: Millimeters per hour.\n",
    "* precipIntensityMax: Millimeters per hour.\n",
    "* precipAccumulation: Centimeters (daily).\n",
    "* NDVI: \"The normalized difference vegetation index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, typically but not necessarily from a space platform, and assess whether the target being observed contains live green vegetation or not.\" (source: Wikipedia).\n",
    "* apparentTemperature is the perceived temperature in degrees Fahrenheit derived from either a combination of temperature and wind (Wind Chill) or temperature and humidity (Heat Index) for the indicated hour. When the temperature at a particular grid point falls to 50°F or less, wind chill will be used for that point for the Apparent Temperature. When the temperature at a grid point rises above 80°F, the heat index will be used for Apparent Temperature.  Between 51 and 80°F, the Apparent Temperature will be the ambient air temperature. (http://www.nws.noaa.gov/ndfd/definitions.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='A closer look'></a>\n",
    "## A closer look"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's continue the exploration of the structure of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features (2013): 26\n",
      "Number of features (2014): 26\n",
      "Number of features in common:  26\n"
     ]
    }
   ],
   "source": [
    "# Check that both years have the same columns\n",
    "features_2013 = df_2013.columns\n",
    "features_2014 = df_2013.columns\n",
    "print('Number of features (2013):', len(features_2013))\n",
    "print('Number of features (2014):', len(features_2014))\n",
    "print('Number of features in common: ', len(set(features_2013).intersection(features_2014)))\n",
    "# features_2013 & features_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountyName                :    150    156\n",
      "State                     :      5      5\n",
      "Latitude                  :   1014   1035\n",
      "Longitude                 :   1014   1035\n",
      "Date                      :    186    186\n",
      "apparentTemperatureMax    :  11290   9941\n",
      "apparentTemperatureMin    :  10916   9960\n",
      "cloudCover                :    101    101\n",
      "dewPoint                  :   8909   8331\n",
      "humidity                  :     93     86\n",
      "precipIntensity           :    661    815\n",
      "precipIntensityMax        :   3131   4184\n",
      "precipProbability         :     97     98\n",
      "precipAccumulation        :   2761   1911\n",
      "precipTypeIsRain          :      2      2\n",
      "precipTypeIsSnow          :      2      2\n",
      "precipTypeIsOther         :      1      1\n",
      "pressure                  :   5116   4655\n",
      "temperatureMax            :  10408   8323\n",
      "temperatureMin            :   9329   8494\n",
      "visibility                :    876    899\n",
      "windBearing               :    360    360\n",
      "windSpeed                 :   2694   2341\n",
      "NDVI                      : 125108 129967\n",
      "DayInSeason               :    186    186\n",
      "Yield                     :    151    137\n",
      "---------------------\n",
      "Total number of observations (2013) : 177493\n",
      "Total number of observations (2014) : 182549\n"
     ]
    }
   ],
   "source": [
    "# Check the unique entries in each feature\n",
    "features = df_2013.columns\n",
    "for feature in features:\n",
    "    n_unique_2013 = len(df_2013[feature].unique())\n",
    "    n_unique_2014 = len(df_2014[feature].unique())\n",
    "    print('{:25} : {:6.0f} {:6.0f}'.format(feature,n_unique_2013,n_unique_2014))\n",
    "print('---------------------')\n",
    "print('Total number of observations (2013) : {}'.format(df_2013.shape[0]))\n",
    "print('Total number of observations (2014) : {}'.format(df_2014.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The numerical columns have a large number of levels. There seems to be no obvious feature that should be treated as a multi-level categorical feature.\n",
    "* There seem to be more counties and locations in 2014 than in 2013. Need to verify.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the min/max 'Yield' for each county. As in the description of the data:\n",
    "\n",
    "\".. this yield label is not specific to a lat/long but is for the county. Multiple lat/longs will have the same yield since they fall into a single county, even if that individual farm had a higher or lower localized yield.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Yield</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountyName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blaine</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clay</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comanche</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Custer</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Douglas</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ellis</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franklin</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garfield</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grant</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamilton</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harper</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hill</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackson</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logan</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marshall</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Yield\n",
       "           Count\n",
       "CountyName      \n",
       "Blaine       2.0\n",
       "Brown        2.0\n",
       "Clay         2.0\n",
       "Comanche     2.0\n",
       "Custer       2.0\n",
       "Douglas      2.0\n",
       "Ellis        3.0\n",
       "Franklin     2.0\n",
       "Garfield     3.0\n",
       "Grant        3.0\n",
       "Gray         2.0\n",
       "Hamilton     2.0\n",
       "Harper       2.0\n",
       "Hill         2.0\n",
       "Jackson      2.0\n",
       "Johnson      2.0\n",
       "Lincoln      2.0\n",
       "Logan        2.0\n",
       "Marshall     2.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df_2013.groupby(by='CountyName')\n",
    "gb_agg = gb.agg({'Yield' : {'Count' : lambda x: len(x.unique())}})\n",
    "diff_counties_2013 = gb_agg[gb_agg['Yield']['Count'] > 1].index.get_level_values('CountyName').values\n",
    "gb_agg[gb_agg['Yield']['Count'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Yield</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountyName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blaine</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carter</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comanche</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Douglas</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ellis</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franklin</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grant</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamilton</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harper</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hill</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackson</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jefferson</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Yield\n",
       "           Count\n",
       "CountyName      \n",
       "Blaine       2.0\n",
       "Brown        2.0\n",
       "Carter       2.0\n",
       "Comanche     3.0\n",
       "Douglas      2.0\n",
       "Ellis        3.0\n",
       "Franklin     2.0\n",
       "Grant        3.0\n",
       "Hamilton     2.0\n",
       "Harper       2.0\n",
       "Hill         2.0\n",
       "Jackson      2.0\n",
       "Jefferson    2.0\n",
       "Lincoln      2.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df_2014.groupby(by='CountyName')\n",
    "gb_agg = gb.agg({'Yield' : {'Count' : lambda x: len(x.unique())}})\n",
    "diff_counties_2014 = gb_agg[gb_agg['Yield']['Count'] > 1].index.get_level_values('CountyName').values\n",
    "gb_agg[gb_agg['Yield']['Count'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, many counties have only one value for the yield (total number of counties: 150 (2013) and 156 (2014)). A few exceptions, as printed above, have more than one value. Before moving on I need to understand what's going on and how these yield values are assigned. Why do most counties have only one value, but a sizeable fraction has several different values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I count 19 counties in 2013 with more than one yield level.\n",
      "Blaine     ['Montana' 'Oklahoma']\n",
      "Brown      ['Kansas' 'Texas']\n",
      "Clay       ['Kansas' 'Texas']\n",
      "Comanche   ['Kansas' 'Oklahoma']\n",
      "Custer     ['Montana' 'Oklahoma']\n",
      "Douglas    ['Kansas' 'Washington']\n",
      "Ellis      ['Kansas' 'Oklahoma' 'Texas']\n",
      "Franklin   ['Kansas' 'Washington']\n",
      "Garfield   ['Montana' 'Oklahoma' 'Washington']\n",
      "Grant      ['Kansas' 'Oklahoma' 'Washington']\n",
      "Gray       ['Kansas' 'Texas']\n",
      "Hamilton   ['Kansas' 'Texas']\n",
      "Harper     ['Kansas' 'Oklahoma']\n",
      "Hill       ['Montana' 'Texas']\n",
      "Jackson    ['Kansas' 'Oklahoma']\n",
      "Johnson    ['Kansas' 'Texas']\n",
      "Lincoln    ['Kansas' 'Washington']\n",
      "Logan      ['Kansas' 'Oklahoma']\n",
      "Marshall   ['Kansas' 'Oklahoma']\n"
     ]
    }
   ],
   "source": [
    "# What states do the counties with various yield levels lie in?\n",
    "print('I count {} counties in 2013 with more than one yield level.'.format(len(diff_counties_2013)))\n",
    "for idx, diff_county in enumerate(diff_counties_2013):\n",
    "    print('{:10} {}'.format(diff_county, df_2013['State'][df_2013['CountyName'] == diff_county].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I count 14 counties in 2014 with more than one yield level.\n",
      "Blaine     ['Montana' 'Oklahoma']\n",
      "Brown      ['Kansas' 'Texas']\n",
      "Carter     ['Montana' 'Oklahoma']\n",
      "Comanche   ['Kansas' 'Oklahoma' 'Texas']\n",
      "Douglas    ['Kansas' 'Washington']\n",
      "Ellis      ['Kansas' 'Oklahoma' 'Texas']\n",
      "Franklin   ['Kansas' 'Washington']\n",
      "Grant      ['Kansas' 'Oklahoma' 'Washington']\n",
      "Hamilton   ['Kansas' 'Texas']\n",
      "Harper     ['Kansas' 'Oklahoma']\n",
      "Hill       ['Montana' 'Texas']\n",
      "Jackson    ['Kansas' 'Oklahoma']\n",
      "Jefferson  ['Kansas' 'Oklahoma']\n",
      "Lincoln    ['Oklahoma' 'Washington']\n"
     ]
    }
   ],
   "source": [
    "# What states do the counties with various yield levels lie in?\n",
    "print('I count {} counties in 2014 with more than one yield level.'.format(len(diff_counties_2014)))\n",
    "for idx, diff_county in enumerate(diff_counties_2014):\n",
    "    print('{:10} {}'.format(diff_county, df_2014['State'][df_2014['CountyName'] == diff_county].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, it seems that counties with the same name can appear in two (or even three) different states. That explaines the different values for the yield. However, some of the counties having two different yield values in 2013 (e.g., Clay) do not show up in the same list in 2014. There are also examples of the opposite (e.g., Jefferson). I can think of two reasons at this point:\n",
    "* the county still shows up for more than one state, but both entries happen to have the same yield\n",
    "* the county only shows up for one of the states\n",
    "\n",
    "Let's investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "County 2013 State 2014                          Yield 2014\n",
      "----------------------------------------------------------------\n",
      "Blaine     ['Montana' 'Oklahoma']              [ 32.4  26.6]\n",
      "Brown      ['Kansas' 'Texas']                  [ 42.   21.4]\n",
      "Clay       ['Texas']                           [ 25.2]\n",
      "Comanche   ['Kansas' 'Oklahoma' 'Texas']       [ 41.5  20.9  30.3]\n",
      "Custer     ['Oklahoma']                        [ 30.3]\n",
      "Douglas    ['Kansas' 'Washington']             [ 30.9  37.8]\n",
      "Ellis      ['Kansas' 'Oklahoma' 'Texas']       [ 32.9  22.4  39.3]\n",
      "Franklin   ['Kansas' 'Washington']             [ 39.9  30. ]\n",
      "Garfield   ['Oklahoma']                        [ 27.3]\n",
      "Grant      ['Kansas' 'Oklahoma' 'Washington']  [ 33.5  23.6  41.7]\n",
      "Gray       ['Kansas']                          [ 46.6]\n",
      "Hamilton   ['Kansas' 'Texas']                  [ 20.5  26.1]\n",
      "Harper     ['Kansas' 'Oklahoma']               [ 27.7  27.5]\n",
      "Hill       ['Montana' 'Texas']                 [ 28.2  43. ]\n",
      "Jackson    ['Kansas' 'Oklahoma']               [ 32.9  25.2]\n",
      "Johnson    ['Texas']                           [ 33.9]\n",
      "Lincoln    ['Oklahoma' 'Washington']           [ 34.9  53. ]\n",
      "Logan      ['Oklahoma']                        [ 31.6]\n",
      "Marshall   []                                  []\n"
     ]
    }
   ],
   "source": [
    "print('{:10} {:35} {}'.format('County 2013', 'State 2014', 'Yield 2014'))\n",
    "print('----------------------------------------------------------------')\n",
    "for idx, diff_county in enumerate(diff_counties_2013):\n",
    "    tmp_states = df_2014['State'][df_2014['CountyName'] == diff_county].unique()\n",
    "    tmp_yields = np.zeros(len(tmp_states))\n",
    "    for ix,tmp_state in enumerate(tmp_states):\n",
    "        tmp_yields[ix] = df_2014['Yield'][(df_2014['CountyName'] == diff_county) & \n",
    "                                          (df_2014['State'] == tmp_state)].unique()\n",
    "\n",
    "    print('{:10} {:35} {}'.format(diff_county, tmp_states, tmp_yields))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "County 2014 State 2013                          Yield 2013\n",
      "----------------------------------------------------------------\n",
      "Blaine     ['Montana' 'Oklahoma']              [ 38.4  18.1]\n",
      "Brown      ['Kansas' 'Texas']                  [ 51.2  13.6]\n",
      "Carter     ['Montana']                         [ 40.9]\n",
      "Comanche   ['Kansas' 'Oklahoma']               [ 14.5  15.3]\n",
      "Douglas    ['Kansas' 'Washington']             [ 44.8  35.3]\n",
      "Ellis      ['Kansas' 'Oklahoma' 'Texas']       [ 19.9  10.2  54.9]\n",
      "Franklin   ['Kansas' 'Washington']             [ 47.4  31.3]\n",
      "Grant      ['Kansas' 'Oklahoma' 'Washington']  [ 27.7  12.7  42.9]\n",
      "Hamilton   ['Kansas' 'Texas']                  [ 12.1  27. ]\n",
      "Harper     ['Kansas' 'Oklahoma']               [ 12.6  10.3]\n",
      "Hill       ['Montana' 'Texas']                 [ 38.8  49.4]\n",
      "Jackson    ['Kansas' 'Oklahoma']               [ 45.4  11.8]\n",
      "Jefferson  ['Oklahoma']                        [ 20.6]\n",
      "Lincoln    ['Kansas' 'Washington']             [ 23.3  48. ]\n"
     ]
    }
   ],
   "source": [
    "print('{:10} {:35} {}'.format('County 2014', 'State 2013', 'Yield 2013'))\n",
    "print('----------------------------------------------------------------')\n",
    "for idx, diff_county in enumerate(diff_counties_2014):\n",
    "    tmp_states = df_2013['State'][df_2013['CountyName'] == diff_county].unique()\n",
    "    tmp_yields = np.zeros(len(tmp_states))\n",
    "    for ix,tmp_state in enumerate(tmp_states):\n",
    "        tmp_yields[ix] = df_2013['Yield'][(df_2013['CountyName'] == diff_county) & \n",
    "                                          (df_2013['State'] == tmp_state)].unique()\n",
    "    print('{:10} {:35} {}'.format(diff_county, tmp_states, tmp_yields))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise reveals that counties that show up in more than one state in one year in most cases show up in more than one state in the next year as well. Exceptions to this rule are:\n",
    "\n",
    "* Clay: ['Kansas' 'Texas'] in 2013, ['Texas'] in 2014\n",
    "* Custer: ['Montana' 'Oklahoma'] in 2013, ['Oklahoma'] in 2014\n",
    "* Garfield: ['Montana' 'Oklahoma' 'Washington'] in 2013, ['Oklahoma'] in 2014\n",
    "* Gray: ['Kansas' 'Texas'] in 2013, ['Kansas'] in 2014\n",
    "* Johnson: ['Kansas' 'Texas'] in 2013, ['Texas'] in 2014\n",
    "* Logan: ['Kansas' 'Oklahoma'] in 2013, ['Oklahoma'] in 2014\n",
    "* Marshall: ['Kansas' 'Oklahoma'] in 2013, does not appear in 2014 at all\n",
    "\n",
    "as well as:\n",
    "\n",
    "* Carter: ['Montana' 'Oklahoma'] in 2014, ['Montana'] in 2013\n",
    "* Jefferson: ['Kansas' 'Oklahoma'] in 2014, ['Oklahoma'] in 2013"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The final question here is whether or not these counties actually are two different counties in two different states, or if they are - for whatever reason - just the same county (with the same locations) assigned to two different states with the remaining values repeated. \n",
    "\n",
    "Now I will go through the counties appearning more than once and compare the locations they show for the different states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2013['Location'] = list(zip(df_2013['Longitude'], df_2013['Latitude']))\n",
    "df_2014['Location'] = list(zip(df_2014['Longitude'], df_2014['Latitude']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blaine     : 0 repeated locations out of 12\n",
      "Brown      : 0 repeated locations out of 12\n",
      "Clay       : 0 repeated locations out of 12\n",
      "Comanche   : 0 repeated locations out of 12\n",
      "Custer     : 0 repeated locations out of 12\n",
      "Douglas    : 0 repeated locations out of 12\n",
      "Ellis      : 0 repeated locations out of 18\n",
      "Franklin   : 0 repeated locations out of 12\n",
      "Garfield   : 0 repeated locations out of 18\n",
      "Grant      : 0 repeated locations out of 18\n",
      "Gray       : 0 repeated locations out of 12\n",
      "Hamilton   : 0 repeated locations out of 12\n",
      "Harper     : 0 repeated locations out of 12\n",
      "Hill       : 0 repeated locations out of 12\n",
      "Jackson    : 0 repeated locations out of 12\n",
      "Johnson    : 0 repeated locations out of 12\n",
      "Lincoln    : 0 repeated locations out of 12\n",
      "Logan      : 0 repeated locations out of 12\n",
      "Marshall   : 0 repeated locations out of 12\n"
     ]
    }
   ],
   "source": [
    "for idx, diff_county in enumerate(diff_counties_2013):\n",
    "    # Get all states the county is assigned to\n",
    "    tmp_states = df_2013['State'][df_2013['CountyName'] == diff_county].unique()\n",
    "    # Get all locations for this county across states\n",
    "    tmp_locations = {}\n",
    "    for ix,tmp_state in enumerate(tmp_states):\n",
    "        tmp_locations[tmp_state] = df_2013['Location'][(df_2013['CountyName'] == diff_county) & \n",
    "                                                       (df_2013['State'] == tmp_state)].unique()\n",
    "    loc_total = sum([len(tmp_locations[x]) for x in tmp_locations.keys()])\n",
    "    loc_unique = len(np.unique(tmp_locations.values()))\n",
    "    print('{:10} : {} repeated locations out of {}'.format(diff_county, loc_total-loc_unique,loc_total))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blaine     : 0 repeated locations out of 12\n",
      "Brown      : 0 repeated locations out of 12\n",
      "Carter     : 0 repeated locations out of 12\n",
      "Comanche   : 0 repeated locations out of 18\n",
      "Douglas    : 0 repeated locations out of 12\n",
      "Ellis      : 0 repeated locations out of 18\n",
      "Franklin   : 0 repeated locations out of 12\n",
      "Grant      : 0 repeated locations out of 18\n",
      "Hamilton   : 0 repeated locations out of 12\n",
      "Harper     : 0 repeated locations out of 12\n",
      "Hill       : 0 repeated locations out of 12\n",
      "Jackson    : 0 repeated locations out of 12\n",
      "Jefferson  : 0 repeated locations out of 12\n",
      "Lincoln    : 0 repeated locations out of 12\n"
     ]
    }
   ],
   "source": [
    "for idx, diff_county in enumerate(diff_counties_2014):\n",
    "    # Get all states the county is assigned to\n",
    "    tmp_states = df_2014['State'][df_2014['CountyName'] == diff_county].unique()\n",
    "    # Get all locations for this county across states\n",
    "    tmp_locations = {}\n",
    "    for ix,tmp_state in enumerate(tmp_states):\n",
    "        tmp_locations[tmp_state] = df_2014['Location'][(df_2014['CountyName'] == diff_county) & \n",
    "                                                       (df_2014['State'] == tmp_state)].unique()\n",
    "    loc_total = sum([len(tmp_locations[x]) for x in tmp_locations.keys()])\n",
    "    loc_unique = len(np.unique(tmp_locations.values()))\n",
    "    print('{:10} : {} repeated locations out of {}'.format(diff_county, loc_total-loc_unique,loc_total))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that all locations within counties that appear in more than one state are unique and no repetitions occur. So the counties just happen to have the same name but are otherwise distinct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Locations'></a>\n",
    "## Locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis above already showed that Longitude and Latitude each had 1014 and 1035 unique entries in 2013 and 2014, respectively. This strongly suggests that the data contain 1014 and 1035 uniqe locations for these years. Let's verify that we actuallly have unique *pairs* of lng/lat values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique locations (2013): 1014\n"
     ]
    }
   ],
   "source": [
    "print('Number of unique locations (2013): {}'.format(len(df_2013['Location'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique locations (2014): 1035\n"
     ]
    }
   ],
   "source": [
    "print('Number of unique locations (2014): {}'.format(len(df_2014['Location'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, it's indeed 1014 and 1035 unique locations in 2013 and 2014, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "882\n"
     ]
    }
   ],
   "source": [
    "loc_2013 = list(df_2013['Location'].unique())\n",
    "loc_2014 = list(df_2014['Location'].unique())\n",
    "\n",
    "matches = set(loc_2013) & set(loc_2014)\n",
    "print(len(matches))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Out of these locations in the 2013 and 2014 data, 882 are actually in common between both years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Format of the data'></a>\n",
    "## Format of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since detailed information on the structure and format of the data is not available, my analysis above provides me with further insight into the data. My current understanding is as follows:\n",
    "\n",
    "* For each location (which falls into a certain county which again lies in a certain state) the data provide weather information for a specific day. We have essentially a location-sepcific timeseries of weather data. \n",
    "* The yield that is recorded corresponds to the yield at the end of the season (i.e, harvest). Despite the fact that it is measured at the end of the season, it is still reported for each individual location and date entry leading up to the hearvest. \n",
    "\n",
    "The given task here specifically mentions to *exclude* the date from the training. But the day in the season is available. Let's check how the season's starting data (and length) may depend on location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gb_2013 = df_2013.groupby(by='Location')\n",
    "gb_2014 = df_2014.groupby(by='Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th colspan=\"2\" halign=\"left\">DayInSeason</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>First_Day_in_Season</th>\n",
       "      <th>Length_of_Season</th>\n",
       "      <th>First_Day_in_Season</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-120.9140928, 45.8405591)</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-120.7332898, 45.8059508)</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-120.4850382, 45.7447958)</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-120.3476399, 45.8191414)</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-120.2148424, 45.977662)</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Date      DayInSeason  \\\n",
       "                           First_Day_in_Season Length_of_Season   \n",
       "Location                                                          \n",
       "(-120.9140928, 45.8405591)          2013-11-30              185   \n",
       "(-120.7332898, 45.8059508)          2013-11-30              185   \n",
       "(-120.4850382, 45.7447958)          2013-11-30              185   \n",
       "(-120.3476399, 45.8191414)          2013-11-30              185   \n",
       "(-120.2148424, 45.977662)           2013-11-30              185   \n",
       "\n",
       "                                                \n",
       "                           First_Day_in_Season  \n",
       "Location                                        \n",
       "(-120.9140928, 45.8405591)                   0  \n",
       "(-120.7332898, 45.8059508)                   0  \n",
       "(-120.4850382, 45.7447958)                   0  \n",
       "(-120.3476399, 45.8191414)                   0  \n",
       "(-120.2148424, 45.977662)                    0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_agg_2013 = gb_2013.agg({'Date' : {'First_Day_in_Season' : np.min},\n",
    "                           'DayInSeason': {'First_Day_in_Season' : np.min,\n",
    "                                           'Length_of_Season' : lambda x: np.max(x) - np.min(x)}})\n",
    "gb_agg_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gb_agg_2014 = gb_2014.agg({'Date' : {'First_Day_in_Season' : np.min},\n",
    "                           'DayInSeason': {'First_Day_in_Season' : np.min,\n",
    "                                           'Length_of_Season' : lambda x: np.max(x) - np.min(x)}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185 935\n",
      "184   6\n",
      "137   2\n",
      "119   1\n",
      "182   3\n",
      "160   1\n",
      "112  23\n",
      " 64   6\n",
      "152   6\n",
      "177   5\n",
      " 96   7\n",
      "144   2\n",
      "103   5\n",
      "  0  12\n"
     ]
    }
   ],
   "source": [
    "los = gb_agg_2013['DayInSeason']['Length_of_Season'].unique()\n",
    "for tmp_los in los:\n",
    "    cts = (gb_agg_2013['Date']['First_Day_in_Season']\n",
    "           [gb_agg_2013['DayInSeason']['Length_of_Season'] == tmp_los].count())\n",
    "    print('{:3} {:3}'.format(tmp_los, cts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185 971\n",
      "151   2\n",
      "169  13\n",
      "152   4\n",
      "156   1\n",
      "170   6\n",
      " 80   6\n",
      "160  13\n",
      "144   6\n",
      " 64  12\n",
      "128   1\n"
     ]
    }
   ],
   "source": [
    "los = gb_agg_2014['DayInSeason']['Length_of_Season'].unique()\n",
    "for tmp_los in los:\n",
    "    cts = (gb_agg_2014['Date']['First_Day_in_Season']\n",
    "           [gb_agg_2014['DayInSeason']['Length_of_Season'] == tmp_los].count())\n",
    "    print('{:3} {:3}'.format(tmp_los, cts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vast majority of locations has a common 'Length_of_Season' of 185 days. In 2013 there are 12 locations with only one entry in 'DayInSeason'. Maybe these locations olny appear once alltogether? Let's check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "105317   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105318   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105689   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105688   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105224   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105690   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105687   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105226   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105223   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105225   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105222   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n",
      "1\n",
      "105685   2014-03-20\n",
      "Name: Date, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "locations = (gb_agg_2013[gb_agg_2013['DayInSeason']['Length_of_Season'] == 0]\n",
    "             .index.get_level_values('Location').values)\n",
    "for loc in locations:\n",
    "    print(df_2013['Longitude'][df_2013['Location'] == loc].count())\n",
    "    print(df_2013['Date'][df_2013['Location'] == loc])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-06-03 00:00:00')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df_2013['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The locations that show up only once do so in the middle of the season, not at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152 2014-01-02T00:00:00.000000000\n",
      "152 2014-01-02T00:00:00.000000000\n",
      "152 2014-01-02T00:00:00.000000000\n",
      "152 2014-01-02T00:00:00.000000000\n",
      "152 2014-01-02T00:00:00.000000000\n",
      "152 2014-01-02T00:00:00.000000000\n",
      "160 2013-12-01T00:00:00.000000000\n",
      "144 2013-12-28T00:00:00.000000000\n",
      "144 2013-12-28T00:00:00.000000000\n",
      " 64 2014-01-13T00:00:00.000000000\n",
      " 64 2014-01-13T00:00:00.000000000\n",
      " 64 2014-01-13T00:00:00.000000000\n",
      " 64 2014-01-13T00:00:00.000000000\n",
      " 64 2014-01-15T00:00:00.000000000\n",
      " 64 2014-01-15T00:00:00.000000000\n"
     ]
    }
   ],
   "source": [
    "for tmp_los in los:\n",
    "    if tmp_los != 185:\n",
    "        cts = (gb_agg_2013['Date']['First_Day_in_Season']\n",
    "               [gb_agg_2013['DayInSeason']['Length_of_Season'] == tmp_los].values)\n",
    "        for ct in cts:\n",
    "            print('{:3} {:3}'.format(tmp_los, ct))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all locations where the length of the season is <185, the first day on record is indeed later than the canonical '2014-11-30' of the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2013-11-30T00:00:00.000000000' '2013-12-01T00:00:00.000000000'\n",
      " '2013-12-07T00:00:00.000000000' '2013-12-25T00:00:00.000000000'\n",
      " '2013-12-03T00:00:00.000000000' '2014-01-13T00:00:00.000000000'\n",
      " '2014-01-02T00:00:00.000000000' '2014-01-22T00:00:00.000000000'\n",
      " '2013-12-28T00:00:00.000000000' '2014-01-15T00:00:00.000000000'\n",
      " '2014-01-06T00:00:00.000000000' '2014-03-20T00:00:00.000000000']\n",
      "[  0   1   7  25   3  44  33  53  28  46  37 110]\n"
     ]
    }
   ],
   "source": [
    "print(gb_agg_2013['Date']['First_Day_in_Season'].unique())\n",
    "print(gb_agg_2013['DayInSeason']['First_Day_in_Season'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise tells us some additional things about the dataset:\n",
    "* The length of the season differs between locations\n",
    "* The first record of a location might have a 'DayInSeason' that is larger than 0, i.e., the season had already started before data tracking had begun."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To follow up on this, let's investigate if there is a clear structure as to when in the season new locations show up in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "186 185\n",
      "186 185\n"
     ]
    }
   ],
   "source": [
    "dis_2013 = df_2013['DayInSeason'].unique()\n",
    "print(len(dis_2013), max(dis_2013))\n",
    "dis_2014 = df_2014['DayInSeason'].unique()\n",
    "print(len(dis_2014), max(dis_2014))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DayInSeason is a consecutive list without missing days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CuYCwCPOikIiISIGiWcRAZAAD0QFejypSNItYPLCYx5+IyGUskGlESomCWWCBjAKtYM7c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\nHxQ/QxowXkLFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165837d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "# Make sure it's sorted\n",
    "dis_2013.sort()\n",
    "# Walk through data\n",
    "locs_per_dis = {}\n",
    "for day in dis_2013:\n",
    "    locs = df_2013['Location'][df_2013['DayInSeason'] == day].unique()\n",
    "    locs_per_dis[day] = len(locs)\n",
    "x = locs_per_dis.keys()\n",
    "y = locs_per_dis.values()\n",
    "ax.plot(x,y, label='2013')\n",
    "\n",
    "# Make sure it's sorted\n",
    "dis_2014.sort()\n",
    "# Walk through data\n",
    "locs_per_dis = {}\n",
    "for day in dis_2014:\n",
    "    locs = df_2014['Location'][df_2014['DayInSeason'] == day].unique()\n",
    "    locs_per_dis[day] = len(locs)\n",
    "x = locs_per_dis.keys()\n",
    "y = locs_per_dis.values()\n",
    "ax.plot(x,y, label='2014')\n",
    "\n",
    "\n",
    "\n",
    "# Style panel\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.set_ylabel('Number of locations',fontsize=18,labelpad=20 )\n",
    "ax.yaxis.set_label_position(\"left\")\n",
    "ax.set_xlim(185,0)\n",
    "ax.set_xlabel('Day in season',fontsize=18,labelpad=20 )\n",
    "ax.tick_params(labelsize=18)\n",
    "plt.legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be no clear trend of how or when locations enter/exit the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "date_2013 = df_2013['Date'].unique()\n",
    "date_2014 = df_2014['Date'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U+d2d+ypJhGWBR8b8/h7gaWBtjPEvgK8CZ1YvNEnV1N7eTi53Den0StralpFO\nrySXu8ZtkZIkSZpz2lvbya3OkW5K0za/jXRTmtzqnFvLGkwjFpSv5Xe3EedzJky5RlgIoRe4JMaY\nK/7+MPBAjPH3ir//AfCVGGPrTAXbaKwRplooFArk83my2axJMEmSJM1phZ4C+Z15souzJsEaTOeW\nTjo2dtCSaqFvsI/c6hxrlq6pdVhVM9vf3Uafz0pNViOskkTYT4F/ijFeFkLIAP8NnBdj3FC8/sfA\nx2KML69S3A3PRJgkSZIkKWksKF9dzuf+qlIsH7gN+MMQwl8B3wL2Ad8bc30pkJ9ukJIkSZIkqfFZ\nUL66nM/KNFXQ99MMHzd3EcNJsMtijM8AhBDSwFlAruoRSpIkSZKkhmFB+epyPisz5RVhMcYXYoyn\nAYuBthjjtSVdTgauqGZwkiRJkiSpsXgYQnU5n5WZco0wVZ81wiRJkiSp8Vjkf2qmM0/O7cScm1+o\nSrH8MYMdDRwNvALYb9AY499NJ8gkMhEmSZIkSY3F0/tmjnOrqarWqZGvAm4AVo00lekWY4ypaUWZ\nQCbCJEmSJKlxeHrfzHFuVYnJEmGVFMv/K4aTYH8DbAKeq0JskiRJkiQ1hJHT+8Yma0ZO7zNZc3Cc\nW1VLJYmwVcBXY4yXzFQwkiRJkiTVK0/vmznOraplyqdGFvv+x0wFIkmSJElSPZvN0/sKPQW6tndR\n6ClUfey5KOknIybt855JldQI+x6QjzFePLMhJYc1wiRJkiSp8cz06X1JLhqfxJMRk/x5T1e1iuW/\nDrgbuDTG+O0qxpdYJsIkSZIkSZWwaHyy+HlPT7WK5f8NsAe4OYTwc+BJYLCkT4wxnja9MCVJkiRJ\n0mQsGp8sft7VV0ki7EggAtuKvx9R/XAkSZIkSdJELBqfLH7e1TflYvkxxmyM8bUHes1ksJIkSZIk\nJVnSi8YnjZ939U25RpiqzxphkiRJkg7GSOHwRS2L2NO3Z3SVyNi2sddK/+e5tPD4ZONN1mdk3Imu\nFXoK/OTpnwBw+CGHj4ur3M+ndj0FwJsOfVPZZ47tUzreVN7LVJ452Zwf6L2Ue9+l76U0zgPFd6D3\nPZ2+U31mJfdUEt9UY5jK97Dcs0u/82PvqWRuJnvmgZ5dyXe30r8DB/P3ZaK5aZRDCKpVI2xksDbg\ndIa3SsJwrbB/iTG+OP0QJUmSJEmVGDlJjgi9g72km9IMDA4Q5gVSpOgd7KVlXgt9Q32kU2kIjDtt\nrvQkuo4qZWoZAAAgAElEQVRlHeQezAHQO9BLOpVmYGh4vHRTerjPmzrI/SQ37pkwPC6R4XjG3E+A\njmUdXPfAdeO2dzWHZvpj/+izR34f+TmiJdXCumXrxj1z5J6xRtrGPnOi95IKKXoHeid8ZnNo5obf\nuqHsqXydWzr5vVt+b7/+Y8cZmZOON3Vw7Y+vHe079r2UznlLqoW9fXsnja9cnBe8+YJx73OivuXm\nfCpzUvr5VDKPU/mc001pBuMgcSjSNK9p3Odb7pmj38OSz7fcs8d+N9csXfOLvy/Fe8p9f6bzzLLP\nnuB7ONkclc5Vue/jnU/eOe7vfLm+E30nysVXOjdJOZWyohVhIYTzgP8PWASMZNYiw0X0/yjGmKt6\nhA3MFWGSJEmSpqPcSXJTMXLaHDCt+yeyILWAEELVxqu1BakFbPvwtv1WzBzxpSN4afClGkamSqWb\n0jyw7gGWX7e8br+fC1ILePCCB6v+Hiaam0Y4lXKyFWFTrhEWQvhN4DqgAHwYWFV8fRh4FrguhHDm\nwYcrSZIkSZrMyElylRo5bW66908kNS/FvDDl/72c81LzUuR35se15XfmSc1L1SYgTVtzqpn7t99f\n1e/7bEvNS83Ie5hobkb+OdGoKtka+afAI8BJMcY9Y9rvCiF8Hfh34M+A26oYnyRJkiSpRLmT5KZi\n7Glz07l/IoNDg4RQdvFFXRocGtzvVL7s4iyDQ4O1CUjT1j/Yz4rDVlT1+z7bBocGZ+Q9TDQ3jX4q\nZSUp+xOA60uSYAAU64PdUOwjSZIkSZpBY0+SW5BaAAxvZ2oOzbSkWkbbRlZ6LEgtGHfaXLmT6C5Z\ncQnppvRobaUFqQWj45X2GfvMdFOaDe/ZMDre2PtH7ildcdIUmsbF1xyax/0c0ZJq2e+Z5VbFlL7P\nyd7LSNvIs0ZiGdEcmtnwng37bQtrb21nw3s27Bdj6Tgjz71kxSXj+o59L6Xz2Ta/bcL4Jouz9H1O\n1Hds29i5OtCclH4+lczjZOOMnauWVAvNoXm/z7fcMyf6rpZ+JiNjj3znj2s/br/v51hTeeb7Xv++\nKc/1RN/DcnFONOdjr294z4Zx72GivwuTfSfKxVc6N0k5lXLKNcJCCHuAT8UYr5zg+keAT8QYX1bF\n+KoqhPDLwKeBM4BXAf8D3AJ8Msa4q6Tvu4HLgDcAvwT8HNgEfD7G+N8lfUOx7zogy/D20ZsZno+9\nk8RjjTBJkiRJ0+apkck9NbKS0wQ9NXL/vy9TnZtrf3wtV9x7BfOb5rNvYB8fe+fHOPu4s2f91Mhy\n78FTIyc2WY2wShJh/wosBlbEGHtKri0CfgS8EGP81YOMd0aEENqBLuBXgK8CW4GlwIXAw8DbY4wv\nFfv+b+B64L+ADcAOYAlwAfAS8IYY49Njxr4auBT4NnAHcBzwQeCeGOPpk8RkIkySJEmSpDmo3KEU\njVBIPgkmS4RVUiPsSuA7wIMhhC8D/1lsX8JwEugo4LcOJtAZ9jHgcGBNjPHmkcYQwr8B3wD+CPhc\nsfl8oA94a4zxhTF9/5PhAwPeB3y52PZ64BLgWzHG94/pmwe+HEL47RjjN2fwfUmSJEmSpCobOVRi\nbCJspJC8ibD6NeUaYTHG7zKc8Hk18BXgzuLry8W2S2KMt85EkFVyCtA7NglWdBPDq7x+f0zb3mLb\nzpK+TwMBGLsi7gPFn+tL+n6tOM7a6YcsSZIkSZJqodyhFI1eSD4Jprw1cvSGEBYDq4DXFpueBP6l\ntMbWXBNCeARojzG+ssy15xje9tkeY3w+hHAK8E8Mr4C7iuGtkW8AvgQMAm8Z2R4aQrgDOA1YGGPs\nLxn3PuDoGOOrJojJrZGSJEmSJM1RnQ930nFrB82pZvoH+8mtzrFm6Zpah6UDqEqNsHoXQvgWcBbw\nphjj5jHtbwQeBCKwPMb4ULH9LcA/AoeNGeZ7DG+t3DPm/s0MJ9AOLfPMm4D3AvNjjANlrpsIkyRJ\nkiRpDmu0QvJJMFkibMpbIxvAeoaTXf8YQvj1EMLhIYRfB77JcD0wgIUAIYRTgX8GngH+gOEE2lUM\nr4S7KYSQGjPuQmDfBM98aey4kiRJkiSpvrS3tvPmw95sEqxBTFgsP4SwieHE0a/FGAeKvx9IjDGe\nVrXoqijGeF8I4RyGa5r9E8O1vgaAvwVeBbwH2B1CaAb+DigAb4sxjiTJbg0hPAH8DfB7DJ8mCcN1\nwCb627BgTB9JkiRJkiTV0GSnRh4JDDGcMBr5va738cUYvx1C+A7D9b5eBjwWY9wRQvgRw0mxnwLH\nUTwQYEwSbMQ/MpwIO5lfJMJ+DhwXQmgurRHG8LbKHeW2RY64/PLLR/98yimncMopp0zz3UmSJEmS\nJGkyEybCYozZyX6vV8WiXGNrhP0K8Cbg7hjjS8UVYQCpMrc3lfwE6GJ4y+QK4F/HjDsfeCPw/cni\nGZsIkyRJkiRJ0sxJUo2w/YQQAsNbJecBVxSbtzK8lfE9IYRDSm75/eLP+8e03VT8eVlJ33VAGviH\nqgUsSZIkSZKkaZvyqZEhhEHgd2OM35jg+jnAN2KM5VZS1VwIoZXhBNYtwH8DhwBrgGXAR2OMfzmm\n758Cnwe6ga8BzwO/CnyA4e2Ty0tOjvwycDHwXeB24PXApcC9k9VM89RISZIkSZJmlqc+Js9kp0ZO\nViNsv3EO8nqt9QEPMZz8OpThVV9dDB8GcOfYjjHGL4QQuhlObv0fhovebwf+GvjU2CRY0YcYTq6t\nA84AdgBXA5+csXcjSZIkSZIm1bmlk46NHbSkWugb7CO3OseapWtqHZZqqJIVYUPA2klWhP0pwyur\nFlcxvobmijBJkiRJkmZGoadAZn2G3oHe0bZ0U5ruy7pdGdbgpr0iLISwGlg9pmldCOH0Ml1fDpwO\n3DftKCVJkiRJkqokvzNPS6plXCKsOdVMfmfeRFiCHWhr5BuBc4t/jsA7i69Se4AfApdULTJJkiRJ\nkqRpyi7O0jfYN66tf7Cf7OJsbQLSnDDpqZExxk/FGOfFGOcxXANs7cjvJa+2GOO7Yow/nZ2wJakx\nFQoFurq6KBQKtQ5FkiQlSKGnQNf2Lgo9/jfIiJE5eaTwyLif050j57g2PvqOj7IgtYC2+W2km9Lk\nVufqajXYTH1vkvx9rKRY/muB5M2QJM2Szs6b6Oi4iJaWLH19eXK5a1iz5pxahyVJkhqcxcT3NzIn\nROgd7B2dm3QqDYGK58g5nn1j5zyEwEfe9hEuWH5BXSXBZup7k/Tv45SL5av6LJYvaUShUCCTOZbe\n3ruB44HNpNMr6e5+lPb2+vmXtSRJqi8WE99fuTkpVckcOcezrxHmfKbeQyPMzVRMVix/0q2RZQb6\npRDCn4QQ/jGEcGcIYVPJ667qhCxJyZLP52lpyTKcBAM4nubmDPl8vnZBSZKkhjdSTHyskWLiSVVu\nTkpVMkfO8exrhDmfqffQCHNzsKa8NTKEkAH+FXg1sAtoA54HfonhhNoOoGcGYpSkhpfNDm+HhM2M\nrAjr7+8mm83WNC5JktTYLCa+v3JzUqqSOXKOZ18jzPlMvYdGmJuDVcmKsM8Ci4HTgKMZLp5/DsMJ\nsc8DLwLvqHaAkpQE7e3t5HLXkE6vpK1tGen0SnK5a9wWKUmSZlR7azu51TnSTem6LSZebWPnZEFq\nAcDoCpoFqQUVz5FzPPsaYc5n6j00wtwcrCnXCAsh/By4Kcb44RDCKxgunL8qxnhX8fp3gZ4Y4+/M\nWLQNxhphkkoVCgXy+TzZbNYkmCRJmlChp0B+Z57s4uzo/8CWa5vKfVO9d7LxFrUsYk/fnorvny3T\neX+l7+1g3+N051jT1whzPlPvoRHmZjKT1QirJBG2D7goxpgLIbQBO4HfjDH+U/H6xcAnY4y/XKW4\nG56JMEmSJEmVKnfiG5EDngJX7ZPiSk9WTDelgcpPVJxpST8hT0qiaiXCfgZcHWO8MoQQGK4H9qcx\nxr8qXv8w8OkY48uqFHfDMxEmSZIkqRLlTnxbkFpACGHSU+CqfVLcZCcrzqUT6JJyQp6k8ap1auRW\n4ASAYvbmfuCiEMIRIYQssA549OBClSRJkiRNpNyJb6l5KeaF8f9rV3oKXLVPipvsZMW5dAKdJ+RJ\nKjXlUyOBW4E/DiGkY4y9wKeBfwb+u3g9Ar9V5fgkSZIkSUXlTnwbHBpkeNPOL5SeAlftk+ImO1lx\nLp1A5wl5kkpNeUVYjPGaGOP/KibBiDFuAt4KXA18EXhnjHHjzIQpSZpLCoUCXV1dFAqFWociSVKi\nlDvxbcN7NhzwFLhqnxRX7mTFdFN6zp1A5wl5kkpNuUaYqs8aYZLqUWfnTXR0XERLS5a+vjy53DWs\nWXNOrcOSJClRqn1q5MHG0YinRkqqX9Uqlv9y4DUxxs0TXD8eeCrG+MK0I00YE2GS6k2hUCCTOZbe\n3ruB44HNpNMr6e5+lPZ2/6NSkiRJUu1Vq1j+F4DrJ7n+deDzFYwnSaoz+XyelpYsw0kwgONpbs6Q\nz+drF5QkSZIkTVElibCVwG2TXN8InH5w4UiS5rJsdng7JIwsDt5Mf3832Wy2dkFJkiRJ0hRVkgh7\nNbBtkus/K/aRJDWo9vZ2crlrSKdX0ta2jHR6JbncNW6LlCRJklQXmiro2wNkJrmeAfYdXDiSpLlu\nzZpzOP30U8nn82SzWZNgkiRJkupGJcXyvwcsBZbGGF8sufYyYAvwWIzx16oeZYOyWL4kSZIkSVJ1\nVatY/lXAa4AfhhDeG0I4qvh6L/DD4rUrDz5cSZIkSZIkqfqmvCIMIIRwAXA10FxyqR+4LMb41SrG\n1vBcESZJkiRJklRdk60IqygRVhzsMOD9wFHFpv8CvhVj3H5QUSaQiTBJkiRJkqTqqmoiTNVjIkyF\nQsGC45IkSTOk0FMgvzNPdnGW9lb/W0uSkqJaNcJGBgshhGXFOmHvLf657OCSJtbZeROZzLGsWnUh\nmcyxdHbeVOuQJEmSGkbnlk4y6zOsunEVmfUZOh/urHVIkqQ5oNIaYe8GrgEyJZfywEUxxn+uXmiN\nzxVhyVUoFMhkjqW3927geGAz6fRKursfdWWYJEnSQSr0FMisz9A70Dvalm5K031ZtyvDJCkBqrIi\nLITwdmAj8EsMF8xfV3xdXWzbGEJ428GHKzW+fD5PS0uW4SQYwPE0N2fI5/O1C0qSJKlB5HfmaUm1\njGtrTjWT35mvTUCSpDmjqYK+nwD+Bzgpxvj02AshhCuBHxX7vLt64UmNKZvN0teXBzYzsiKsv7+b\nbDZb07gkSZIaQXZxlr7BvnFt/YP9ZBdnaxOQJGnOqKRG2EnAdaVJMIBi29eAt1QrMKmRtbe3k8td\nQzq9kra2ZaTTK8nlrnFbpCRJUhW0t7aTW50j3ZSmbX4b6aY0udU5t0VKkqZeIyyEsBf48xjj1RNc\n/xDw+RjjwirG19CsESZPjZQkSZo59XJqZC3irJe5kaTpmKxGWCWJsAeAPuAdMcaBkmtNwD3A/Bjj\n8oOMNzFMhEmSJEnJ1rmlk46NHbSkWugb7CO3OseapWsa7pmSNJuqlQg7D7gOuBf4AvCfxUtLgI8A\nvwqsizHmDjrihDARJkmSJCVXLU639ERNSUkwWSJsysXyY4x/G0I4GvgThpNepa40CSZJkiRJUzNy\nuuXYpNTI6ZYzlZSqxTMlaS6p5NRIYox/FkLIAauB1xabnwQ2xhj/q9rBSZIkSVKjqsXplp6oKSnp\nKjk1EoAY43/FGK+MMV5UfF1lEkySJEmSKlOL0y09UVNS0k25RpiqzxphkiRJkiY6wXEmT3b01EhJ\njWxaxfJDCBum8awYY+yYxn2JZCJMkiRJUjme7ChJ0zfdRNjQNJ4VY4ypadyXSCbCJEmSJJXyZEdJ\nOjiTJcImrBEWY5w3jZdJMEmSJEk6CCMnO441crKjJOngVFwsX5IkSZI0czzZUZJmjsXya8itkZIk\nSVJ1jBR/X9SyiKd2PQXAmw59E+2t7eOu7enbM+7nVPqW9il95kjB+UJPgZ88/RMADj/k8P2eNfbn\nSFKr9P6RZ3/7kW/zuXs/R9O8JvoG+/jsqZ/l5MzJ4+53m6QklTetGmGaeSbCJEmSpIM3UlieCL2D\nv6ir1ZJqYd3ydeQezAHQO9BLc2imP/aP/hzRHJq54M0XlO07drzr33M9a5au2a+YfcebOrj2x9fu\nN+bYZ430TafSDDJIHIosbFk4fP+yjnHPTqfS9A/2wzxoCk28NPjS6DjppjSABfQlaQImwuYoE2GS\nJEnSwSlXWH4mpZvSPLDuAZZft3zWnjlZLBbQl6T9TatYviRJkiTNdeUKy8+keWEe92+/f1afOREL\n6EtS5UyESZIkSapb5QrLz6ShOMSKw1bM6jMnYgF9SarchImwEMKTIYTfHPP7J0IIS2cnLEmSJEk6\nsPbWdnKrc6Sb0ixILRh3rSXVwiUrLiHdlB6tq9UcmoHhultjNYfmCfuOHS+3Osdx7ceNPrNtfhvp\npjSXrLhkv/6lzxpZRbYgtYCWVAvNoXnc/WOfvSC1gObQTEuqZbRtZJyRfrnVObdFSlKFJqwRFkIY\nAH4vxvgPxd+HgLUxxm/MYnwNzRphkiRJUnU02qmRpX3K3W8STJLKm1ax/BBCN9AZY/w/xd+HgN+J\nMXbOWKQJYyJMkiRJkiSpuqabCPsycAmwGXgeOAV4BHhmkmfFGONpBxVtgpgIkyRJkiRJqq7JEmFN\n5RqL/gx4ATgdyAARaAcWVj1CSZIkSZIkaYZNuCJsv47WCKs6V4RJkiRJkiRV12QrwiY8NbKM3wd+\nWJ2QJEmSJKm+FHoKdG3votBTmFK7JGnumfKKsHE3hfAK4LXFX/87xvhcVaNKCFeESZIkSfWhc0sn\nHRs7aEm10DfYR251jjVL10zYLkmqnWkVy59goBOALwO/WnLpXuCDMcbN044ygUyESZIkSXNfoadA\nZn2G3oHe0bZ0U5oH1j3A8uuW79fefVk37a3ttQhVksT0i+WXDrIUuA9YANwKbC1eWgKcCdwbQnhb\njHHrBENIkiRJUt3J78zTkmoZl/BqTjVz//b7y7bnd+ZNhEnSHDXlRBjwaaAfeHvpyq9ikuyeYp+z\nqxeeJEmSJNVWdnGWvsG+cW39g/2sOGxF2fbs4uwsRidJqkQlxfLfCfx1ue2PMcaHgWuAk6sVmCRJ\nkiTNBe2t7eRW50g3pWmb30a6KU1udY7j2o8r2+5qMEmau6ZcIyyE0Av8SYzxrye4fjFwVYwxXcX4\nGpo1wiRJkqT6UegpkN+ZJ7s4Oy7ZNVG7JKk2qlIsP4SwFdgWY/z1Ca7/X+CIGOOSaUeaMCbCJEmS\nJEmSqmuyRFglWyP/Dvi1EMI3QghLQgip4mtpCOEfgHcB11chXkmSJEmSJKnqKlkRlgK+AbwPiMBQ\n8dI8IAA3Ax+IMQ6VH0GlXBEmSZIkSZJUXVXZGjlmsFXAe4DXFpueBL4bY7zzoKJMIBNhkiRJkiRJ\n1VXVRJiqx0SYJEmSJElSdVWrRpikOaBQKNDV1UWhUPCZDSJJ71WSpLmq0FOga3sXhR7/fSxJjcxE\nmFRHOjtvIpM5llWrLiSTOZbOzpt8Zp1L0nuVJGmu6tzSSWZ9hlU3riKzPkPnw521DkmSNEPcGllD\nbo1UJQqFApnMsfT23g0cD2wmnV5Jd/ejtLe3+8w6lKT3KknSXFXoKZBZn6F3oHe0Ld2Upvuybtpb\n/fexJNUjt0ZKDSCfz9PSkmU4YQJwPM3NGfL5vM+sU0l6r5IkzVX5nXlaUi3j2ppTzeR35msTkCRp\nRpkIk+pENpulry8PbC62bKa/v5tsNusz61SS3qskSXNVdnGWvsG+cW39g/1kF2drE5AkaUZNKREW\nQkiHEP53COGkmQ5IUnnt7e3kcteQTq+krW0Z6fRKcrlrZnQLXVKeWStJeq+SJM1V7a3t5FbnSDel\naZvfRropTW51zm2RktSgplQjLIQwD+gFPhRj/OqMR5UQ1gjTdBQKBfL5PNlsdtYSJkl5Zq0k6b1K\nkjRXFXoK5HfmyS7OmgSTpDo3WY2wKRfLDyH8FLguxviFagaXZCbCJEmSJEmSqqtaxfJvAH43hDC/\nOmFJkiRJkiRJs6epgr4/BH4LeCiEcA3wOLC3tFOM8Z4qxSZJkiRJkiRVTSVbI4dKmkpvDECMMaaq\nEVgSuDVSkiRJkiSpuibbGlnJirDfr1I8kiRJkiRJ0qyb8oowVZ8rwiRJkiRJkqqrWsXyJUmSJEmS\npLpVUSIshHB4CGFDCOFnIYS+EMKpxfb2YvubZyZMSZIkSZIk6eBMOREWQngt8GPgbGArMFoUP8ZY\nAE4Ezqt2gJIkSZIkSVI1VFIs/wpgCFgK9ALPlly/HTizSnFJkiRJkiRJVVXJ1sjTgWtijE8B5Sq8\ndwOvqUpUkiRJkiRJUpVVkghrA56e5HoLla0wkyRJkiRJkmZNJYmwp4Alk1x/C/DTgwtHkiRJkiRJ\nmhmVJMK+A/xBCGHpmLYIEEI4G3gfcHMVY5MkSZIkSZKqJsRYrtxXmY4htAH/BmSBe4B3AXcyvGVy\nBfAQ8PYY40szEmkDCiHEqc6/JEmSJEmSDiyEQIwxlLs25RVhMcbdwFuBvwVOBALw/7N373FylfXh\nxz9fYJcMCUGQRQRhpyoaKaDgDWqtREWLWivSSvGusdYqeKu9iFbxWm21Itb8sLpeakuMirdq1YqE\nKl4w1ku84aV1AgKWwQuasJBInt8fz5nkZDIzOzs7u7Ob83m/XvPa5MxznvOc85wz88z3POd5TgPu\nDqwFVhsEkxZes9lk48aNNJvNURdFWnS8PiRJkiSVzebRSFJKv0opPS+lNAHcATgcuH1K6dwiUCZp\nAa1bt57JyVWcdtqzmJxcxbp160ddJGnR8PqQJEmS1K7vRyM1fD4aqbloNptMTq5ienoDcAKwiVpt\nNZs3X8XExMSoiyeNlNeHJEmSVF1DeTSylNnjImJdRFxZvNZFxOPmXkxJs9FoNBgfr5N/5AOcwNjY\nJI1GY3SFkhYJrw9JkiRJnfQdCIuI5RHxGWAdcBZwTPE6C1gXEZ+NiOXzU0xJ7er1Otu2NYBNxZJN\nbN++mXq9PrpCSYuE14ckSZKkTmbTI+w1wEOAtwBHpJQOSSkdAhxRLFtdpJG0ACYmJpiaWkuttpqV\nK0+iVlvN1NRaH/uS8PqQJEmS1FnfY4RFxPXA51JKZ3V5/wPA76aU7jjE8u3VHCNMw9BsNmk0GtTr\ndX/kS228PiRJkqTq6TVG2H6zyGclsKHH+5cBj5hNwSTN3cTEhD/wpS68PiRJkiSVzebRyE3kMcG6\nOQb41tyKI0mSJEmSJM2P2QTCXgr8aUT8QfsbEfGHwDOA84ZVMEmSJEmSJGmYuo4RFhHv7LD43sBx\nwPeB7xXL7gHcndwb7L9TSmvmoZx7JccIkyRJkiRJGq5eY4T1CoTtGGBbKaW07wDrVZKBMEmSJEmS\npOHqFQjr+mhkSmmfAV6LOggWEYdFxEURcXVE3BoRmyPigog4qEv6R0bEZyLi5xGxNSK+HxFv6ZAu\nIuIFEfG9iJgu8n9DRBww/3slSZIkSZKkfsxm1sglLSImgK8AhwMXAd8hP+b558ADI+IBKaVbSulf\nDrwc+BTwMuBm4GjghA7ZXwCcC1wCvIH8uOhzgXsBD52nXZIkSZIkSdIsVCYQBrwEOAo4O6X0/tbC\niPgScDHwQuC1xbKHkoNgL00pvbZXphFxLHAO8MGU0uNKyxvAhRHxJyml9w15XyRJkiRJkjRLXccI\n65g44neA5wDHALcH2p+3TCmluwyveMMTEd8A7ppSWtG2PICtwLUppWOKZZeRe3UdmVLaERHLgZs7\nDegVEa8GXgw8MKX0xdLy/YGfAZenlB7VpUyOESZJkiRJkjREA40R1iGTPwU+DzwWGAeuBja3va6e\nc2nnz/7ALe0Li0jUNHDniDikGNfrgcCVwDMi4ifAr4EtEbEuIg5ry+I+wA5gY1u+twLfAO479D2R\nJEmSJEnSrM3m0cjzyIGdh6eUbpyn8syn7wBnRMQJKaVNrYURcS/gYCCRxwDbAewLnAI8DPg7YBM5\nOPZ84PiIuE9pPLEjgBtTSts7bPNa4JSI2C+l9Jt52i9JkiRJkiT1oe8eYcAdgKklGgSDPKB9Aj4Q\nEadHxFERcTrwPmBbkeYA4MDi34cCz0kpvSql9NGU0ouAV5EfmXxKKd8DgFu7bPOWUhpJkiRJkiSN\n0GwCYd8j95xaklJKVwBnASuAj5Mf5fwocBnwiSLZr8iPSULuGfavbdm8hzwu2qmlZTeTH7vsZFkp\njSRJkiRJkkZoNo9GvpY8C+K7UkrXzVeB5lNK6ZKI+BBwPLnn1/dTSjdGxJXAb4AfASuL5L/o8Ljj\n9cXfckDwOuAeETHWIf2R5Mcmuz4Wef755+/896mnnsqpp546u52SJEmSJElSX/oOhBVBpJXA9yLi\nI0ADuG3PZOlVQyzf0BWD45fHCDscOBHYUIz7dUtEXA3cKSKWlcYCA7hT8feG0rKNwGnA/YAvlPLd\nH7gXcHmv8pQDYZIkSZIkSZo/fQfCIuIewCvIPame1CVZIo+jtSRERAAXkh8RfU3prfeSJwf4M+DN\npeXPJu/jJ0rL1hdpn08pEAY8E6gB/zb0gkuSJEmSJGnWIneQ6iNhxGXkXk8vBj4P/KJTupTS5qGV\nbogiYjnwFeDDwI+Bg4CzgZOA81JKry+lPRC4EjgGeDvwTfKskY8HLiXPnJlK6S8EngN8BPgP4Fjg\nXODzKaWH9ChT6vf4S5IkSZIkaWYRQUopOr43i0DYFuANKaXzh1i2BRMRY8C7gZOBO5IHsN8IvDGl\ndGmH9IeQe7f9IXkGyZ8AFwOvTilta0sb5B5hzwTqwI3k2ShfnlLqOlC+gTBJkiRJkqThGlYg7Frg\ntSmltw6zcFVmIEySJEmSJGm4egXC9plFPuuBxw6nSJIkSZIkSdLCmk2PsHsA7wGuIw8w/2P2nDWS\nlFV7sFwAACAASURBVNLVwyzg3sweYZIkSZIkScM1rEcjd5BnTIzib0cppX0HKWQVGQiTBtdsNmk0\nGtTrdSYmJkZdnCWn1/EbxbG1PiVJkiQNS69A2H6zyOeV9AiASdJCWbduPWvWPJvx8TrbtjWYmlrL\n2WefNepiLRm9jt8ojq31KUmSJGmh9N0jTMNnjzBp9prNJpOTq5ie3gCcAGyiVlvN5s1X2ZOoD72O\nH7Dgx9b6lCRJkjRswxosX5JGrtFoMD5eJwdNAE5gbGySRqMxukItIb2O3yiOrfUpSZIkaSH1/Whk\nRPxeP+lSSp8bvDiS1Fu9nh+fg020ehBt376Zer0+0nItFTMdv4U+ttanJEmSpIU0mzHCLqe/McIc\nLF9ST3MZGH1iYoKpqbWsWbOasbFJtm/fzNTU2hnzcTD2bKbjN8ixhcGP76D1uZh5rkmSJEmL12xm\njXxKh8X7AXcBngo0gLellN4zrMLt7RwjTFU0rIHRZxNscDD2PQ1z1shhHN+9JXjkuSZJkiSNXq8x\nwoYyWH5EHAx8DTjfQFj/DISpakYxMLqDsc8vj+8uHgtJkiRpcZj3wfJTSr8A3gH81TDyk7R3cjD2\nvY/HdxePhSRJkrT4DXPWyF8Adx5ifpL2MrsPjA4LPxj7wmyzSjy+u3gsJEmSpMVvKIGwiFgGPAn4\n6TDyk7R3ag2MXqutZuXKk6jVVs/7wOij2GaVeHx38VhIkiRJi99sBst/Z5e3DgFOASaAv0wpvXFI\nZdvrOUZYNcxlEPBB113sA48Pq3yzyWeYx2RvrZd2wz6+w0ozbMPe5lKrZ0mSJGlv02uMMFJKfb2A\nHV1eNwJfBB7fb16+dh7TpL3bxRe/L9Vqh6SDDjop1WqHpIsvft+8rzuXbS4lo9rPqtTLsMvbT36j\nOEZLrV4kSZIkzayIt3SMxQxl1kgNxh5he7e5zCA36LpVmbVuVPtZlXoZdnn7yc8ZRSVJkiQNy7zP\nGilpT3OZQW7Qdasya92o9rMq9TLs8vaTnzOKSpIkSVoIBsKkeTKXGeQGXbcqs9aNaj+rUi/DLm8/\n+TmjqCRJkqSF0DMQFhEfm+XrowtVcGmxm8sMcoOuW5VZ60a1n1Wpl2GXt5/8nFFUkiRJ0kLoOUZY\nROyYZX4ppbTv3IpUHY4RVg2tGeRWrFjBli1b9vjbaWa58jrXXHMNAEcdddRu6TvNTNdtW53WAXqW\nq7ztE088cbf1e6VtlXOu+bXK3a2c/e7DIOvMZl/a07Qf637yK5er0/qDlG825fz617/e8xzrpw57\n5Tesc6yfOpzpepnPc7bftM4mKUmSJM2vocwa2e0FPAi4kjyD5E/mml+VXjhrZGW0Zqar1Y5PUEtj\nY0cnqKVa7bf2mKmuPIvd+PhBaWxsRarV7lykPz7Vaoekc8557h4z3bXPfnfOOc9r+/9zO+TbKs8d\nEtTS+Pg9SuXbP8EBCe6axscP2rl+qyzd0pb3rz1fODzBAWlsbEWP/O6wc1/b97+Vpnzc9jy2nfYl\nrzM2dmAaHz9oxrS5nPvPOp/2+mmVOx+X1n4/b7fylvNrr6tu5exVvn7Sto7fOec8L42PH7SzfDC+\nxzm5+/HtfE6Uz7ld+XXfZnnfyufEntvcs967nROdzsNu25zN+b37OdueT6/zu9M2O1/zkiRJkoaL\nHrNGziWIcxzwCeA24JfAS4DaoPlV8WUgrBpuuOGGVKsdkuCbCVLx95AEG3b+rdUOSTfccEOXtAcV\n6crLasX6+f/Llt2ubb0NRZru68DBCW7okvZ2xat9/Q93KEuntIcUadvzbe33wQmWdcivU1k67X/O\np7/9bm2zvZyzSTvbfLodq4Nmcaw39Mh/Nsd8NmkPLtLmc/K73/1u6fjeULzfTzk7pZ35nFi27Hal\nbbauj05pO50Tcz0m3eqsvM1yPr3On0713PmalyRJkjR8vQJh+w3Qvewo4FXAE4og2IXAq1NKP5tt\nXlIVtGamm57eNTMdTALLd/4tz1S3Z9o7ADXKM9vBnYr18//33fewtjTLgaN6rgN1oNElbXt+rfVv\nKtabKe1kkbY939Z+14EbO+TXqSyd9j/n099+t7Y5l7Szzad1rDrtS+rj+JXrqt/66XbMe6W9U9vy\nOrCV1jn5la98pXQ+bgR+a4ZytvLrlHbmc2LffbeWttk6Tzql7XROzPWYdDu/y9ss112v86dTPXe+\n5n1EUpIkSVpYfc8aGREHR8QbgO8DTwLWA6tSSi8wCCZ112lmOthMDjjkv62Z6jqn/T/gmrZlPynW\nz/+/7bYb2LGjnGbrjOvkIFi9S9obirK1r39Qsd5MaTcXadvzbe13A2h2yK9TWTrtf86nv/1ubbO9\nnLNJO9t8Wseq0778pI/jV66rfuun2zHvlba9LI0ifT4n73e/+5XOxzrw4xnK+ZMeaWc+J2677YbS\nNlvnSae0nc6JuR6Tbud3eZvl49Xr/OlUz52veUmSJEkLrFtXsdYL2B/4a+Dn5HHAPg3ca6b1fPlo\npHbZNebRcSmPF3RUglpatqzedYywlStP3Dke0rJl9ZTHFzputzGkVq48cY+xslrL2tOU/79rnKVc\nnv32m0i7j7d0VILxlMdQustuYyi1ytItbXn/2vOFO6T2McLa82utU6sdt8f+t9KUj1v7se28L3md\nsbEVxdhevdPmcraPwTVzPu310yp3Pi7tY6Mdt0d+7XXVrZy9ytdP2tbxO+ec5xZjet0ltcYIaz8n\ndz++nc+Jcrl35dd9m+V9K58T7dvsVO/dzolO52G3bc7m/G6Vb7/9lqc//uOzisdxe9VL53ounz/L\nlt0u/c3fnJc+/elP+3ikJEmSNA/o8WjkTLNGrgHOB44Avgb8TUrps/MZmKsSZ42sltnMINdrdsd+\nZo3slmZvmzVyptky52vWyH7K1WnWyG77vVhnjex07s5l1sh+jk0/10uvOhxk1she5+M111zDZZdd\nzoUXvo3x8dxj87zz/oIzzzxjoPP7kks+zCtf+Xq2bz8MuI6xsX14z3vewdlnn9Xr40OSJEnSLPSa\nNXKmQNgO8kAnXwXeX/y7l5RSetOgBa0aA2GStLg1m00mJ1cxPb2BPNbXJmq11WzefNWsx/fqlBec\nyrJliauv/oHjhUmSJElD0isQ1s9g+QHct3jNJAEGwiRJe4VOk10MOtB9o9Fgn33aB9jPkwQ4cL4k\nSZK0MGYKhK1ekFJIkrQI7T6BRe7FNehA9/V6vTS5Q6tHWIPbbksOnC9JkiQtkJ6BsJTSfy1UQSRJ\nWmwmJiaYmlrLmjWrGRubZPv2zUxNrR2o91Yrr6c+9ffYtu1Q4HrGxvbhne98h73BJEmSpAXSc4ww\nzS/HCFNLp8HTBxl/qH3w/H7ekzSzYV5D5QkF2icJkCRJkjR3Aw+Wr/llIEwA69atZ82aZwNHMj39\nI2q1w4GbmJpa2/dMcq08WrPaldft9Z4kSZIkSXsbA2GLlIEwdZ5FbjVwCbXamX3NTNdrVjtgaDPe\nSZIkSZK0FPQKhO2z0IWRtEtrRrrdZ5GbBJbvnJlukDxa6/Z6T5IkSZKkqjEQJo3Q7jPSUfzdDGzt\ne2a6Tnm01u31niRJkiRJVWMgTBqh1ixytdpqarXjgZNZtmwltdqZfc9MV85j5cqTqNVW71y313uS\nJEmSJFWNY4SNkGOEqcVZIyVJkiRJGg4Hy1+kDIRJkiRJkiQNl4PlS5IkSZIkqfIMhEmSJEmSJKkS\nDIRJkiRJkiSpEgyESXPQbDbZuHEjzWZz1EWRJEmSJEkzMBAmDWjduvVMTq7itNOexeTkKtatWz/q\nIkmSJEmSpB6cNXKEnDVy6Wo2m0xOrmJ6egNwArCJWm01mzdfxcTExKiLJ0mSJElSZTlrpDRkjUaD\n8fE6OQgGcAJjY5M0Go3RFUqSJEmSJPVkIEwaQL1eZ9u2BrCpWLKJ7ds3U6/XR1coSZIkSZLU036j\nLoC0kJrNJo1Gg3q9PqdHGCcmJpiaWsuaNasZG5tk+/bNTE2t9bFISbPS+kxasWIFW7Zs2e3vNddc\nA8BRRx21x3tbtmyZ8+eYJEmSVEWOETZCjhG2sNatW8+aNc9mfDz35pqaWsvZZ581pzyHFViTVD2t\nzyS4HdPT1+/8bBobm2D79v8D9gWOAK4tljUZG1vJ9u2/ola7K3DtUD7HJEmSpL1NrzHCDISNkIGw\nhePg9pIWk12fSZcAZwK7PpvgQUWq/yotWw1MAY8HvoyfY5IkSVJ3DpavynNwe0mLya7PpOVAnfJn\nExwGTLYtmwRuAo7CzzFJkiRpcAbCVAkObi9pMdn1mbQVaFD+bIIbgM1tyzYDBwHX4OeYJEmSNDgD\nYaqE1uD2tdpqVq48iVpttYPbSxqZXZ9JZ7Js2UrgZMbHjwVOZmzsQOBm4BTgrsWy5cDj2W+/FcDJ\n1GrH+zkmSZIkDcAxwkbIMcJ26TVz2kyzpLUPWN9rAPtu2+knba8B8ctpWzO9nXjiif5AldSTs0ZK\nkiRJw+dg+YuUgbBs18xpRzI9/aOds6LtPoNak1rtcOCm3WZJa58Jcs2aJzI19a89Z4Zs316vfFuz\nufWaoa09LRwJXMfY2D685z3vcEY3SZIkSZIWkIGwRcpAWKfZHC8HHkF5VrQ8W1prZrVLqNXOZPPm\nqwD2mAkSTgb+AziVTjOqdZo9spX/nvnuOZtb9/w6zfx2KsuWJa6++gf22pAkSZIkaYE4a6QWrT1n\nc1xO+6xoeba05Tv/tmZJ6zQTJNypSJv/3z6jWud1uuW752xu3fPrVO46++57mDO6SZIkSZK0SBgI\n00jtOZvjVtpnRcuzpW3d+bc1S1qnmSDhJ0Xa/P/2GdU6r9Mt3z1nc+ueX6dyN7jtthuc0U2SJEmS\npEXCQJhGqjybY612PPCInbOi7ZpBbTnwCJYtW0mtdubOWdI6zQR5zjl/Sq12ZteZIffc3sk98t01\nm1u3Gdo6pc2zvJ3C2Nh23vnOi3wsUpIkSZKkRcIxwkbIMcJ2WahZI7ttz1kjJUmSJEnaOzhY/iJl\nIEySJEmSJGm4HCxfkiRJkiRJlWcgTJIkSZIkSZVgIEySJEmSJEmVYCBMI9NsNtm4cSPNZnPURZEk\nSZIkSRVgIEwjsW7deiYnV3Haac9icnIV69atH3WRJEmSJEnSXs5ZI0eoqrNGNptNJidXMT29ATgB\n2ESttprNm69iYmJi1MWTJEmSJElLmLNGalFpNBqMj9fJQTCAExgbm6TRaIyuUJIkSZIkaa9nIEwL\nrl6vs21bA9hULNnE9u2bqdfroyuUJEmSJEna6xkI04KbmJhgamottdpqVq48iVptNVNTa30sUpIk\nSZIkzSvHCBuhqo4R1tJsNmk0GqxYsYItW7ZQr9cNhkmSJEmSpDlxjDAtShMTE/zoR//Lve/9u84e\nKUmSJEmS5p09wkbIHmHOHilJkiRJkobLHmFalJw9UpIkSZIkLSQDYRoZZ4+UJEmSJEkLyUCYRmYp\nzR7ZbDbZuHEjzWZz1EWRJEmSJEkDcoywEar6GGEtrdkjF+uskevWrWfNmmczPp57sE1NreXss88a\ndbEkSZIkSVIHvcYIMxA2QgbCFj8H9JckSZIkaWlxsHxpQA7oL0mSJEnS3sNAmNSDA/pLkiRJkrT3\nMBAm9bCUBvSXJEmSJEm9OUbYCDlG2GBag+uvWLGCLVu2zHmQ/fb8OuXbT5pu+S7WSQAkSZIkSdob\n9RojbL+FLow0F60ZHOFIpqd/RK12OHDTwDM57srvdkxPX79zZsj2fCcmJrj00st2S1ur3RW4tuO2\nnWlSkiRJkqTFxx5hI2SPsNnpNIMjrAYuoVY7c9YzOe7K7xLgTKB7vkDXtO2zSDrTpCRJkiRJo+Os\nkdordJrBESaB5QPN5Lgrv+VA73x7pW3ftjNNSpIkSZK0OBkI05LRaQZH2AxsHWgmx135bQV659sr\nbfu2nWlSkiRJkqTFyUCYlozyDI612vHAySxbtpJa7cyBZnLcld+ZLFu2EjiZ8fFjO+bbKW2tdnzH\nWSSdaVKSJEmSpMXJMcJGyDHCBjOKWSMH2bazRkqSJEmStPB6jRFmIGyEDIRJkiRJkiQNl4PlS5Ik\nSZIkqfIMhEmSJEmSJKkSDIRJkiRJkiSpEgyEacloNpts3LiRZrM56qJIkiRJkqQlyECYloR169Yz\nObmK0057FpOTq1i3bv2oiyRJkiRJkpYYZ40cIWeN7E+z2WRychXT0xuAE4BN1Gqr2bz5KiYmJkZd\nPEmSJEmStIg4a6SWtEajwfh4nRwEAziBsbFJGo3G6AolSZIkSZKWHANhWvTq9TrbtjWATcWSTWzf\nvpl6vT66QkmSJEmSpCXHQJgWvYmJCaam1lKrrWblypOo1VYzNbXWxyIlSZIkSdKsOEbYCDlG2Ow0\nm00ajQb1et0gmCRJkiRJ6sgxwgoRcVhEXBQRV0fErRGxOSIuiIiDZljvzyNiR0TcFhGHdHg/IuIF\nEfG9iJgu8n9DRBwwf3tTPRMTE9z3vvc1CCZJkiRJkgZSmR5hETEBbAQOBy4CvgMcBzwL+DbwgJTS\nLR3WuyPwPSCAFcBESunnbWneDJwLXAJ8CrgH8Fzgcymlh/Yokz3CJEmSJEmShqhXj7D9FrowI/QS\n4Cjg7JTS+1sLI+JLwMXAC4HXdlhvLfAj4LvAE9rfjIhjgXOAD6aUHlda3gAujIg/SSm9b4j7IUmS\nJEmSpAFU6dHIU4HpchCssB64BXha+woRcQbwKHKvsdu65Pv44u8FbcvfDtwMPHHA8kqSJEmSJGmI\nqhQI258c8NpN8WziNHDn8vhfEXEg8BbgopTSV3vkex9gB/mxy3K+twLfAO4796LvXZrNJhs3bqTZ\nbI66KJIkSZIkqUKqFAj7DnBwRJxQXhgR9wIOLv57dOmtvyePC3beDPkeAdyYUtre4b1rgUMjokqP\noPa0bt16JidXcdppz2JychXr1q0fdZEkSZIkSVJFVCkQdgGQgA9ExOkRcVREnA68D9hWpDkAICIe\nADwTeEFK6dcz5HsAcGuX924ppam8ZrPJmjXPZnp6Azfd9N9MT29gzZpn2zNMkiRJkiQtiMr0VEop\nXRERZwEXAh8n9/b6DfAO4A7AY4BfRcQY8DbgPzuMJ9bJzcBEl/eWldJ0dP755+/896mnnsqpp57a\nxyaXpkajwfh4nenpVqe8Exgbm6TRaDAx0e0QSpIkSZIkDUdlAmEAKaVLIuJDwPHAgcD3U0o3RsSV\n5KDYj8gzQN4deGFE3KVYNYr0kMcSOyil9OPi/9cB94iIsQ6PRx5JfmzyN93KVA6E7e3q9TrbtjWA\nTcAJwCa2b99MvV4fabkkSZIkSVI1VCoQBjsHx9/U+n9EHA6cCGxIKd0SEUeTHxn9VKfVga8AW4CV\nxbKNwGnA/YAvlPLdH7gXcPnw92JpmpiYYGpqLWvWrGZsbJLt2zczNbXW3mCSJEmSJGlBRI4LVVNE\nBLAeeCzw4JTS5yLieOCYDsnPAR4EPA34ZUrpY0UexwHfBD6UUvrjUt7nkscle2JKaV2X7acqHv9m\ns0mj0aBerxsEkyRJkiRJQxURpJSi43tVCcRExHJyb64PAz8GDgLOBk4CzkspvX6G9d8FPBmYSCn9\nvO29C4HnAB8B/gM4FjgX+HxK6SE98qxkIEySJEmSJGm+9AqEVenRyG3AN8jBrzuSB7DfCDw8pXRp\nn3l0i1o9jxxceybwCOBG4M3Ay+dSYEmSJEmSJA1PZXqELUb2CJMkSZIkSRquXj3C9lnowkiSJEmS\nJEmjYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEyS\nJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVg\nIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIk\nSZVgIEx7jWazycaNG2k2m6MuiiRJkiRJWoQMhGmvsG7deiYnV3Haac9icnIV69atH3WRJEmSJEnS\nIhMppVGXobIiInn8567ZbDI5uYrp6Q3ACcAmarXVbN58FRMTE6MuniRJkiRJWkARQUopOr1njzAt\neY1Gg/HxOjkIBnACY2OTNBqN0RVKkiRJkiQtOgbCtOTV63W2bWsAm4olm9i+fTP1en10hZIkSZIk\nSYuOgTAteRMTE0xNraVWW83KlSdRq61mamqtj0VKkiRJkqTdOEbYCDlG2HA1m00ajQb1et0gmCRJ\nkiRJFdVrjDADYSNkIEySJEmSJGm4HCxfkiRJkiRJlWcgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJ\nkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBM\nkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmV\nYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmS\nJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEyS\nJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVg\nIEySJEmSJEmVYCBMkiRJkiRJlWAgTJIkSZIkSZVgIEySJEmSJEmVYCBMkiRJkiRJlbDfqAsgzYdm\ns0mj0WDFihVs2bJl5996vc7ExMSoiydJkiRJkkbAQJj2OuvWrWfNmmcDt2N6+nrGx+ts29agVjsc\nuImpqbWcffZZoy6mJEmSJElaYJFSGnUZKisiksd/uJrNJpOTq5ievgQ4E9gAnABsAlYDl1Crncnm\nzVfZM0ySJEmSpL1QRJBSik7vOUaY9iqNRoPx8TqwHKiTg2AUfyeB5YyNTdJoNEZSPkmSJEmSNDoG\nwrRXqdfzY5CwFWiQe4JR/N0MbGX79s3U6/WRlE+SJEmSJI2OgTDtVSYmJpiaWkutdibLlq0ETmZ8\n/FjgZJYtW0mtdiZTU2t9LFKSJEmSpApyjLARcoyw+eOskZIkSZIkVVOvMcIMhI2QgTBJkiRJkqTh\ncrB8SZIkSZIkVZ6BMEmSJEmSJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJkiRJ\nkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJ\nkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWC\ngTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmS\nJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFVCpQJhEXFYRFwUEVdHxK0RsTkiLoiIg9rS\nPTEi1kXEDyNia5HuoxFxvy75RkS8ICK+FxHTRf5viIgDFmbPJEmSJEmSNJNIKY26DAsiIiaAjcDh\nwEXAd4DjgGcB3wYekFK6JSL2B6aBrwOfAH4M3LFIdyTwpJTSxW15vxk4F7gE+BRwD+C5wOdSSg/t\nUaZUleMvSZIkSZK0ECKClFJ0fK8qgZiIuIAcrDo7pfT+0vI/AS4GXppSem1E7Av8Tkrp823rH0YO\nnv0mpXTH0vJjgW8Bl6SUHldafg5wIfD4lNL7upTJQJgkSZIkSdIQGQgDIuIbwF1TSivalgewFbg2\npXTMDHl8EDgDuGNK6YZi2auBFwMPTCl9sZR2f+BnwOUppUd1yc9AmCRJkiRJ0hD1CoRVaYyw/YFb\n2hcWkahp4M4RccgMedwJ2Ab8srTsPsAO8mOX5XxvBb4B3HcOZd7rXX755aMugubIOlw6rKuly7pb\nuqy7pcu6W9qsv6XPOlw6rKulq6p1V6VA2HeAgyPihPLCiLgXcHDx36O7rRwRjwDuB7wvpbSt9NYR\nwI0ppe0dVrsWODQi9ptTyfdiVb3w9ibW4dJhXS1d1t3SZd0tXdbd0mb9LX3W4dJhXS1dVa27KgXC\nLgAS8IGIOD0ijoqI04H3kXt5AXSc5TEijgHeC1wDvKjt7QOAW7ts85ZSGkmSJEmSJI1QZQJhKaUr\ngLOAFcDHgc3AR4HLyLNDAvyqfb2I+C3gs8BvgNNTSj9rS3Iz+bHLTpaV0kiSJEmSJGmEKjNYfksx\nOP7xwIHA91NKN0bElcC9gINSSreU0taBy8nBswenlDZ1yO9TwEOAA9ofj4yIK4BjUkp36FKWah18\nSZIkSZKkBdBtsPzKjV1VDI6/M6AVEYcDJwIbegTBHtopCFbYCJxGHj/sC6X19ycH1y7vUZaOlSJJ\nkiRJkqThq8yjkZ0UvcMuJB+H15SWTwIbgJXAw1JK3+iRzfri7/Pblj8TqAH/NrQCS5IkSZIkaWCV\neTQyIpYDXwE+DPwYOAg4GzgJOC+l9Poi3Qpyj7FJ4C3kHl/t/jOl1CzlfSHwHOAjwH8AxwLnAp9P\nKT1kvvZJkiRJkiRJ/atSIGwMeDdwMnBH8gD2G4E3ppQuLaWbBP53huxWp5Q+V1onyD3CngnUgRvJ\ns1G+PKXkQPlaNCJiX2A78I6U0jNHXR5JkiQtfbYxJS0llXk0MqW0PaX0hJTSXVJKB6SUDk0pnV4O\nghXpNqeU9p3h9bm2dVJK6U0ppXuklGoppaNSSn85qiBYRDwoInaUXr+JiJ9HxLci4t0R8fBRlKut\njI+OiHdGxPciYktEXBsRn+lVtoh4ckR8LSJujoifRsTbI+LQDunuFhFviIjPRsQvimPwsi554lM+\nGQAAHaBJREFUTkTEuyLimxHxs4iYjoj/iYj3RsTxw9zn2epQj+XXbRFxvxGW7THFcSvX339GxGk9\n1nlqRHy9OMbXR8TbIuKQDulWFfV3WUT8stjf8/osV0TEV4p1PjSXfZyriLhdsa87IuIJIyzHERHx\n4oi4PCKuK+rr2xHx952Of7HOHSPiXyLihuJ62xgRf9Ql7Z9FxL8W58JvIuK2WZTt9NI5fdKg+zgs\npWvuhSMsw+9FxFsjYlNE3FTUwRUR8Sc91nlERHyhqNufRcT7i3Eu29MdHhGviYhPFvnuiIh3zqJs\n64t1uo2ZOTKL5XprFxEnFp9n/118D/+8+Iz684joOE5rRNw/Ii6NiF8V58AnI+KeHdItj4iXR8RH\nI+KaYt8vm0XZXl+ss8eM2QstIg6MiL8tjtOvImJrRHyn+Jw6bA753rM4RkfPYh2vwd5lsI3ZZxuz\nw7oREV8q1vnYXPZxrjrUo23Mzvn+pMcxWjmsfZ6NWCTfd7GI2pcR8ZQe5/OFw9rnuQjbmO3pL+9x\nbS3Ib4LKDZZfMReTH9UM8iyZdwceAzw5Ii4F/iilNKoG8NuAm4CPAt8HDgGeBnwyIl6SUvq7cuKI\neAHwRvLYbc8F7gT8BXByRNwvpTRdSn4KuYfe/wBfBR7coxwHA3cFPg1sBqaBY4A1wB9HxINSSlfO\ncV/nqlWP7X600AUpeTu55+NHgR+Q628N8OmI+OuU0j+UE0fEXwKvBz5Lfmz4aOCFwP0j4uTyRBXA\nA9i9/lbPolzPA1YBi6Gr6xOB/YEtwNMZ3XiBfwC8DPgE+fHtX5Mn93g+cFZE3DeldEMrcUQcTJ74\n41DyNXct8Hjg/RHxtJTSe9ry/xty/X8dWA4c2U+hIuIAYG1RnhUD793e5/XkY/hh4FvkY3oWcHFE\nrE4p/Vk5cUQ8FvgA+fi/iPzY/wuAKyLiPimln5aS3x14MXA1eaiA0/stVEQ8CjiT3Jt6MVos11u7\nvyLPLP0R4J+BfYFHAW8FHk1bHUTEyeTvuZ8ALyV/f58DfD4iTkkpfaeU/FDg5cBPgf8GOs5Q3UlE\n3It8nvy62MbIRMTdyN/BRwEfAt5B7lVyMvkz/WkR8QcppS8PkP29yMdoA/m874fXYH9sY87cxmz3\nHOC3WRxtlBbbmL0l4NvA6zq8t3WW+zUsi+X7brG1LxN5zO+r2pZ/f/a7ttdaTN9vCWiSz5f2dshM\nT+cNR0rJ1172Ah4E7ABe2OG9AP6heP8TIyzjqR2WLSN/eN0CHFRafnvyh/2XKB7nLZY/qtiPv2nL\n53bAyuLf9y7SvGyW5btPsd67FmM9ziHPfYs8/3ke6q8G/JDcSD+wtPwwcmPh823pH1OU5UVtyw9u\nrQ/cv0hzXh9lOpr8Jfz8Yp0PjaruivJ8Dfgm8ErgN0B9nrazH7B/j/fvARzWYfma4jj9fdvyvwdu\nAx5RWrYPcCX5C+uA9uNe+ve/A7f1We43kb8s/6HY3kmjrK+iTEO/5gYowwPLn3Ol5ZcXx+nYtrq/\nltxgqJWW37M45y5qy2M5cPvi37cv9vWdfZRpOflGwQXkMTY3jbquOpRxQa63Acp1CjDeYfl726+z\nYvlXgF8Ch5eWHUH+Uf+ptrTjwBGl//8auKyPMu1TbOcj5B/+vxrh8amRf6TcAvx+h/dPAn5BDvZN\nDJD/U4vj/HuzWMdrsHdZbGMO0MYkB9duYlcb5WOjOj4z1eMc8tzr2pjANeSxoUdWVx3KZPtyz20+\nhVl+1o+g3mxj7p5+A/C/o6yTyjwaqSxlfwlcAfx+RPxO+f2IWBn5cYkfRsQtRdfGiyPit9rzioix\niPiryF2RtxZdjDdGxHP6KMflHZbdAnwcGCNHlVvOIH8BviUVV06R/uPki/OJbfn8Ms39LmTrzvG2\nOeazYCLi7KJ7a+uxki9FxBk90j8sIr5cdE2+LiL+MSJq/WyrS/1Nk+8K7U/uVdfyWHID9C1t6T9C\nbti3198vUkq/7qccbf4f8D1yT4uRKrr03ovcs+Gd5B8HT++SdkfkRzgeUtTZ1sjd+i+IPMlHOe35\nRfpji/q6htyL8f7dypJS+l4q3ZErac14e1zb8rOB/0kp7bxDnFLaQa6/Q4BHtOXfby+L8n7ch9zL\n5XnkHyCLUqkb+5M7vPfuiNjRtuzyiPjfyF3/10V+XGhrRHwqIo5pz6OTlNLny59zJR8s/pbr60Hk\nMS/fkUo9FlJK3yQ3as6KPGZLa/nWlNLP+ilHm9eSG6svHWDdedfv9RYRk9HlMabStXV02/IHFdfl\nzcV1+aaIuEe3fNqllL6UUur0PbK+KOfO+oyIu5Bvwrw/le6yppSuI9+RfWiUHhNMKW0r3put55F/\nwJw7wLrD9gzy98WbUkqfan8zpfQ14Dzyj92/bH8/Iv60+B77dfHaFBGvKN57Ofl8ACg/gtHzUUSv\nwcHZxuzpreReVm8eYN2Rq3obMyL2jTyh2kjZvpxZRKyIPDb4omYbc+d+RUQcONv1hsFAWHVNkT88\nH9laEPlZ9y8BzyI3Fs4hfzitBr4cEUeV0o4B/wn8HflO7d+SG6tfJTcqBtXaxv+Vlt2n+NvpsYgv\nA6siP2Y1sIjYLyJuH/n55geSu4r/DFgMz5UfUJSt/NrtyzgiXkfuGv0LckP5r8l3PS+JiE4Dlt6P\n/KF3Bbn7+BfIdyk/PMeytuqv/MXYq/6uBH47Ivafy0YjP9v+MODPyHcgRm0NcCvwrymlBnAZ+W5V\nN/cmH/svkh/H+Bz58YyPtKVLxevfyI2TNxTprx+gjHtcaxFxOLnLdLdrLYD7DrCtnYovzbeTe7fM\n9XxbCN0eYUkd3kvkO2KfIz/a9WLyZ+ipwEciYi6PoHX6bLxvsc1u9bUSuNsctknkcWKeAzw/pbRY\ng5azvd462aM+I+J3yY/sTZIDEa8lf569pz3tALrVJ/S+/u49l41GnhDolcD5KaVr5pLXkPwR+Vi+\nvUead5OvpzPLCyPiX8mPwO0AXk1+bOOzpXQfIj+OSvH+E4vX2wYsq9dg/2xjlkQeA+mRwLO6/Agd\nJduYM3sAuSfar4rgwzuL9tIo2L7sLsg9x34F3BIR34hFNGZoF5VuY5LPiS3ATZHHILskIu4+00rD\n4hhh1dUaaLV8Ar+KPOvl/VNK324tjIh3k5+PfwW77jq8gBwpfk1K6W+HUaDIgwGfAXwupbS59NYR\nxd9rO6x2LfmD7wjmNp7Bw8kfni0/JHfN/k6X9AvpFeQfLmXvIz9X32ok/xXwipTSK0pp/iki/h14\nXUS8N+0+xsXxwKNSSp8s/n9RRPwT8OcR8diU0qwHmi/uUv0B+dGcn5Temqn+9iHfcWjMdpvFdm9H\nflzkrSmlr5fvToxC0eA6G/hwSukXxeJ3kJ+/f3hK6dMdVjsOeExKqXUOXhQR1wPnRsTjUkrvL28C\n+DlwWnEnbVCvIH/Bvbu0bKa6gj7HAOvhReS7uX84x3wWymwbFoeSHwd4484MIm4kj8vwUOAzsy5A\nxBHAn5LHNLmi9Fa/9fW92W6z2O6+5HP3UymlSwbJY74NeL316x/JQZZTWt9JEbEW+K85lnk5uXfT\nL8lj4LQcQb4me33XzfX6+3/k8+hNc8xnWH4b+HVKqet4ICml6Yi4CjguIg5IKd0cEY8jfwf+S0rp\nqV3W+1ZEfIl87Vya2iY6mg2vwVmzjblruyvJvcAuSiltHCSPeWYbs7dN5Gv+KvLj6KvJPVkfHHn8\nuE49ouaF7cuebiYH8S4jB0p/i3wD4b0RceeU0qvmkPd8qmwbk9zj9gryNXYbOQB7Lvna+t2F+A1u\nIKy6Wt26yzOePJ4cZb4+Im5fWj5Njvw+rC3tz8kNmzmLPDPPh8gfZM9oe/sAgJTSrR1WvaWcZg6+\nRP4AqQHHki/EyyPi9JTSV+eY91z9M/mxmLLy4IRPIP9Ye29bvUEO7j2C/OFyeWn5d0oNlJbXAc8m\nNxRn1UiJ/LjOJeSofvvdwQPIT0x0mu1lGPX3RvKz6ovlkZEzyYNJTpWWfZh8vTyd3MOk3fdLjZSW\n15Hv2p0BlBsqCbhgLo2UiPgLck+Mi1JK5R/1rXqYl2st8qNfLyM3qAfq8r4EtLr5l11Gbuwcwywb\nKZEfJfkw+S7go9quo3mtL/KPnzuTf3wsVoNcbzMqPtPuA7yv/KM5pXRbRLwZ+J2uK/fOdx9yY30S\nODul9MvS2/N9/Z1N/h5/wBx/5AzTSvrrcdBqsxxEbic8gfxZuMfjksPmNTgQ25i7/AP587+v2a9H\nwDZmDymlR7YtWh8RXyD3DH45OdiyUGxfdpFS+gBt53FEvI08kcxLI+I9e0m7c69pY6aU1rQt+lAR\nXL+cfCNy3mcgNhBWXa3Gya8AImKCPLDdw8gDFrZL5GhtyzHA17uMfTIrkWcRuRQ4nDyA4v+0Jbm5\nSLd/h4bKsnKaQaWUfk7+IAH4RPHIxSbynZZ7zSXvIfhhSumyHu+vIt/x6na3MrHnbGJ7RO9TSj+J\niC3kRnffiobRZ4EJ4PSU0o/bktyck8W+HRoqc6q/iDiVPBPU4xbRIyNryLMdXV0Eflo+DZwZEYcU\n51tZp/r4aUT8ks718cNBCxcRzyAPWPrv7DlGUKseOj1GMIxr7SLyHac3zpRwCbuuw+dia8yE9h8R\nPRV3fz9KHjD8ySmlL7Ylmbf6ioi7kh9HemVb74nFZpDrrR+tMYt+0OG9gWagKh5beBc5qHFe2514\nmN/6PJjcC+wdafQzIZf9it2DJd200txU/L0rcH1KqVN7ZWi8BgdmGzPn+UBy4O0JA44rthBsY85S\nSum9EfFq8uOuCxkIs305Cyml7RHxBvL37sPIv+mWur2ijdlNSumKiPgcsLrLZ/JQGQirrhOKv60G\nfatr5qXkOwULMp160UD5LLn7/KPb7h60tAYDPpI9p1M9kvwlPMiAwV2llK6PPP334yLioJTSTTOu\nNDpB7hF1Ot2fNf92l+Vz23DEIeT6uzP5TsLnOyQr11/73ZgjyXc3BhmDAPLgs18Fvl5qFLQ+15YX\ny345yOCNg4iIOvlZfdjzx3Krbp7I3MeeG7RR/nTy+DifIk9t395oLNdVu9ayTl2k+9n2GcBDyIHL\nemkog9YX91ERcVOHHymj1Gssl27fn53uSrf0/blaaqA8GHh6Smldh2Tl+mo/3+ZUX+Rg5c+Aj5au\nrSDv93ixbGvafersBTXA9TZIfQ5FEQSbKspzfkrp9R2SXUf3xx9b33WD1uf55Du372irz1pRvLsA\nt7Y9crQQvg08sHh0pePjkcUd61VAI6U01EZ3L16Dc2IbM/sn4BvAxrY6hDw214K2UQZU5TZmL5vJ\nAYQFYftyYA3yOXzoPOQ9V1VuY/bSID8afzC7904dOgNh1fUM8gX4ieL/TfJ4JStTShv6WP8H5AFE\nx1JK2wcpQKmBsor8/PqlXZJuJHeFPoU9Gyknk7v9zkfjuDW7zWJ5hKSbH5IDDI1ZBBHu0b4gIu4E\nrGDPY9xR0UC5jNzA/IMe581GcpftU9izkXJ/4LtziPgfTf5x134HKwGnkc/TN5MHa10IrfFNnsGu\nngtlrynStDdUOtXH4eRp2vuqj5kUjZS3kwcgPqPTdVvcJbyWfF21O6X4O+ijwkeT66U1y9FumyZ3\nzU4RMTFgD5750CrHIR3eu0uHZUNRaqA8FPjTlNK/dEm6kXwsT2FXj9aWU8i9MTr1aOrH0eTxIbqN\n0fBD8oDXjx4w/2GY7fU2m/psFH87Ddq6qv8i7hYEewrwqh5jlbTGDzqFXbMdUlqWyI95DOJo8qMP\nX+ny/g/JP2ZP6PL+fPkQ8HvkOuz26NhTyDP9lcfI+gHw6OLzolevsIEGJvcanDPbmNnR5N5xndoo\nDybv51vJj6ktVlVuY/ZyF3YfWHy+2b4cTGucwoWsq35VuY3Zy93Iwff5/y2QUvK1l73IUdQdwAs7\nvLcPeSaQHcDH2t57CznSfGaXfCdK/35RkccrBizj7cgN+mng92dIeyiwlTyOV5SW/0FRhhf3WPfe\nRZqXdXn/sC7LjwV+DVy5GOuxLd3JRbr3A/v02kdg3yLtbcAj29L9U7H8sX2U7WDg6+Q7R6fNkPYO\nRbor2pafUZTlL3use/8izXld3n80eers8uuPinW+XGzj+AWqryDfIfxGjzQvK47xvUvLWvXxh21p\nLyiWP6607OXFsqNnWbankr9UPg3sP0Pav28/P4rPja+Qeycs77HuvwO3dXnvzh3q6rHkabZvKz5T\nHgvstxD11aWMu11z5MDBNuDjbel+pyjzbW3LNwD/2yHfyV6fQ21px4FPFvW1Zoa0+5Hvxv0YOKC0\n/J7F+m/rse7tizK9s8v7D+5SX/9HDhKdQR5EflR1Nej1dh2wqcO5ubX92irO+ZuBetsx/0KRdsb6\nLNaZKtK/so+0XyEHDA4vLTuC/MPn0zOs+2vyQNKd3rt/l/r8drHvZwAPGUE9HkBuSN8CPLzD+yeR\nG8PXs/t3Wetz/l2U2gUd1n9ske4xsyiT12DvfbaNuSvNTG3MR3Wpwx3FtX4GcMJC1+FM9diWrspt\nzIO7LH9esd6bFqiubF/2aF8W7x/SYdlB5EDuNHDEQtTVDPtvG3PX+yu7fJ48sljv3xeiTuwRtne7\nd+yaNvZA8p3tx5DvUH2KPABm2UvIF9/6iPgAOZCwjXyBPYIcqW/dkXgzuZHw0sgzyvwnuSH728Dd\nUkoPo7dLgROBi4Hbx57T234xFeMApJRujIi/JQ84+tmIWAfcidzL57tFWXaKPEPPc8l33FpdNx8U\nES8p/v2xlNK3in+/OCJOI9+1bJC/bI4DnkS+EBfy2f+BpJS+HBGvIg8W/7WI+CD5R8MdyVPfPpT8\nYVu2CVgXEf9MHrPpoeRGw2dSf7P5fJb8QfivwGEd6u8LKU/rTErp/yLifODvisdN30c+p14AfIu2\nQR8jzwJ5Drn+WtP5nhq7nqX7cErpu0XeH2svWOyaNfK6lNJcp+qejYeTy/v2HmkuIT+itIbde3Z8\nizwQ7TvIX9oPJg+KuiHtOY7QrETEo8njItxEHkj0j2L3GZa3pJTKM9e9Dvhj8ixEbyJ/CT6e3OBf\nk1La2pb/o8jnAuRxeyhda79MKb0VIOXHnva4+xgRxxf/vCyl9LVB93M+pJS2Rp7RbE1EXEwewPMY\n8uOdm5if3jMXk8+lz5Cn/26/tja1Pr9SSr+JiOeRr6krIuLt5Ibf88k/ls9vzzwiXsquKbgB7lmq\nr8+l4tGT1GXMmIh4I3mWv4W8tjoZ9Hr7J+DVEfFJ8vTxRwJ/Rr4G26dufxH5u+1LkWeLvAl4HLl3\nEvTR26gYn+Rp5Mejvt+hPv8npVSemvx55DuvV0TEW8jfSecUf/+iQ/7nkOs8inJNlurzmymljwOk\nLuOCRcS55B8+I6nPlGeAfDS5Yf7xiPgQ+Tr7DflH6hPJd50fk0qzs6WUPhgR64EnA3eLiI8BvyC3\nc05LKbWuzY3k7/KXFD1MtgI/Til16xkHXoP9so3ZuY350VTMitm6/toV38E/XQR1OKMKtjE/knbN\nVvf0iHgy+XxukIMIDyafm99nSJM59MH2ZY/2ZWs/I+K/iv1tzRr5NPLYgC9MKQ11CJ1hqHIbkzz7\n6j9GHhz/f9n1nf8Ecv29YAj7OrOFiLb5WtgXOeJ8W+m1ndxA/Bb57mnXuyvkwe9eAnyT3GC8idwt\n/23AfdvSjgMvLvK9mXzX9krgz/oo420zvJ7cYZ0ns+sO0U/JXwiHdkjXiozPmDf5C+H95ItwS5H3\nD4C1wG8tknp8QZ/pH0W+I/Mz8t2PBvmxiaeX0uxb5Pk28qODVxb1fB15ho5aH9tp5dHr9fgO6z2V\n/GOwVX9vA27fId1dZqi/PfLuUr5LFri+3k/+IP/tGdJdVdTR/sX/d5Afg3ow+YfBVnIj8wLa7o4x\nwB270jrdXp3uLt2RPCPSDUV9fZU85kOn/N81m7x7lO+kUVxnbWU5taiP55WWLSfPqtUsPiP+i3yH\n/F3Ab9rW30AObLTnO1ns49/2UYYfz1Bfe9zxI/+I/GJRvp+Re9l1/Pya4drq527ij8kBllHX1aDX\n277kxvi1pXP7kd2ureKc+GKR9nryj+JWL4IX9VHODTPU5x53S4v8P0MOAN0E/AdwzwHOl453YjuU\n76ZFUJ8HUvzQLvZ7KzkI8Xq69Nwu1vvzog63FMfqG8BL29I8idzz7ZZ+jovX4Izbt43ZZxtzhvJ9\ndMTXnG3MGfIGHkh+hKxR7MdW8mfJq4ADF7CubF/OkDc5kL2R3Fa7tcj/34GHjvI6ayvjqdjGbKVb\nRQ6w/ZD8nT9d/PtC4I4LVSdRFEaSNAIRsQN4d0rp6TMm1rwq7m5+BHhaSuk9oy6PFqeIOJN89/tP\n0hzvqEuSNB9sXy4utjEXn31GXQBJkhaJ1oDk35opoaqhGFS2/P/9yI9MbSc/xiBJkjQT25iLjGOE\nSZIqLSLOBu4DnAt8LS2ysco0GkUQbHNE/Bt5PJhDyWOEHQ+8LpXGrJIkSWpnG3PxMhAmSaOV6GPQ\nbc2rt5LHNfggHQYkV2VtJ4+B82jyuCZBDog9O6X0tlEWTJKkGdi+XBxsYy5SjhEmSZIkSZKkSnCM\nMEmSJEmSJFWCgTBJkiRJkiRVgoEwSZIkSZIkVYKBMEmSJEmSJFWCgTBJkiRJkiRVgoGw/9/e3YTe\nVpVxHP8+piWEhtqLToQolBqYYIMoSzBCoYmDaGIGhgVFQSUV1cACByFGVNCopAysQREERuUlLXrD\nyiAVgpCgN+iFAikkKleDc/7173arm95r5P584LA5e6+9ztqzw49nPRsAAACATRCEAQAAALAJgjAA\nAAAANkEQBgAAAMAmCMIAAAAA2ARBGAAAAACbIAgDAAAAYBMEYQAAAABsgiAMAAAAgE0QhAEAAACw\nCYIwAAAAADZBEAYAAADAJgjCAAAAANgEQRgAAAAAmyAIAwAAAGATBGEAAI9DM3PZzDx86PPnmfnt\nzNw7Mx+fmSse5fzPm5kbZub8E7VmAICT7dT/9QIAADipbqu+UE11RnVhdVX16pk5Ur1irfXgI5j3\n4uqG6s7qJydorQAAJ5UgDADg8e2etdZth0/MzFurm6rrq09VL38E8061Hv3yAAAeO7ZGAgBszNp5\nW/X16sqZeWHVzJw3M++fme/vt1E+NDP3z8zbZ+Zv/xtn5obqlv3Xuw5tv7zl0Jgnzsy7Zua+/Ty/\nm5nPz8zFj+WzAgAcpiIMAGC7PlZd2q4i7JvVRe22TX6ueqA6rbqyel/1zOr1+/s+W51Xvba6sfrh\n/vwDVTNzavWl6gXVJ6sPV0/Zj//GzLx4rXXPSX42AIB/IggDANiuH+yPF+yPd621nnXUmA/NzK3V\ndTPznrXWL9da983Mt9oFW0fWWl876p43VS+prlhrHTk4OTMfqe6vbq4uP9EPAwDwn9gaCQCwXQdN\n8s+sWmv98eDCzJw2M2fNzDnVl9v9b3z+cc57dbsqse/PzDkHn+r06o7q0pl50ol6CACA46UiDABg\nu87cHx+smpknVO+srqme3a4h/oFVnXWc8z6nXej162NcO2iw/9Tq5//legEAHhVBGADAdl20Px70\n+PpA9cZ2b5K8sfpV9afqknZ9wo53N8FU91Zv6R/DtMOOFZIBAJxUgjAAgO26rl2F1u3776+qvrrW\nuvrwoJm54Ogb+3tl17H8qHraWuvOE7JKAIATRI8wAICNmZlTZubm6kXV7Wutb+8v/aWjKrhm5snV\nm48xze/3Y88+xrVbq3Nn5vp/8ftPf6RrBwB4NFSEAQA8vl0yMwcVXmdUF1ZXVedXX2zX2P7AZ6rX\nzcynqyPVudW11W+OMe93qoerd8/M2dUfqh+vte6uPli9rLppZi6vvtKuD9n51Uurh/ZHAIDH1Kz1\n76raAQD4fzQzl7ULoA483K6K62fVd6vb1lp3HHXP6dV7q1dWz6h+Wn20+l67tz1eu9a69dD4a6p3\ntGusf1r1ibXWa/bXTqne0K7x/nP3t/yiuns/7siJfF4AgOMhCAMAAABgE/QIAwAAAGATBGEAAAAA\nbIIgDAAAAIBNEIQBAAAAsAmCMAAAAAA2QRAGAAAAwCYIwgAAAADYBEEYAAAAAJsgCAMAAABgEwRh\nAAAAAGzCXwECQIWUgfFAKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e68450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "\n",
    "# Make sure it's sorted\n",
    "date_2013.sort()\n",
    "# Walk through data\n",
    "locs_per_date = {}\n",
    "for day in date_2013:\n",
    "    locs = df_2013['Location'][df_2013['Date'] == day].unique()\n",
    "    locs_per_date[day] = len(locs)\n",
    "x = locs_per_date.keys()\n",
    "y = locs_per_date.values()\n",
    "ax.scatter(x,y,label='2013')\n",
    "\n",
    "# Make sure it's sorted\n",
    "date_2014.sort()\n",
    "# Walk through data\n",
    "locs_per_date = {}\n",
    "for day in date_2014:\n",
    "    locs = df_2014['Location'][df_2014['Date'] == day].unique()\n",
    "    locs_per_date[day] = len(locs)\n",
    "x = locs_per_date.keys()\n",
    "y = locs_per_date.values()\n",
    "ax.scatter(x,y,color='g',label='2014')\n",
    "\n",
    "\n",
    "# Style panel\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.set_ylabel('Number of locations',fontsize=18,labelpad=20 )\n",
    "ax.yaxis.set_label_position(\"left\")\n",
    "# ax.set_xlim(185,0)\n",
    "ax.set_xlabel('Date',fontsize=18,labelpad=20 )\n",
    "ax.tick_params(labelsize=18)\n",
    "plt.legend();\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, this plot reflects what I see for the 'DayInSeason' plot above. There is clear baseline around ~955 locations, with positive spikes up to 985 locations. At the very beginning of the season there is a slow ramp up over a month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As further check to understand the data, let's see how often each unique location appears in the data. Locations with only very few records have potentially limited use for a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186    901\n",
       "185     10\n",
       "184      2\n",
       "183     26\n",
       "178      5\n",
       "174      3\n",
       "169      2\n",
       "153      6\n",
       "13       2\n",
       "9        1\n",
       "8        2\n",
       "7        4\n",
       "6        4\n",
       "5       21\n",
       "4        6\n",
       "3        5\n",
       "2        2\n",
       "1       12\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_records_2013 = df_2013.groupby(by='Location').agg({'Location': {'Count' : lambda x: x.count()}})\n",
    "n_records_2013['Location']['Count'].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186    949\n",
       "185     13\n",
       "178      3\n",
       "177      6\n",
       "154      7\n",
       "153      4\n",
       "14       1\n",
       "13       1\n",
       "11       3\n",
       "10       1\n",
       "9        8\n",
       "8        4\n",
       "7        4\n",
       "6       12\n",
       "4       13\n",
       "3        6\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_records_2014 = df_2014.groupby(by='Location').agg({'Location': {'Count' : lambda x: x.count()}})\n",
    "n_records_2014['Location']['Count'].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zfv163vOe9/DII48wb948mpqa6O3tpbe3d6Svyy+/nCOOOIL3ve99LFmypKpxqrR5vIWt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\n9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd05090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "\n",
    "klocs = n_records_2013[n_records_2013['Location']['Count'] >= 153].index.get_level_values('Location').values\n",
    "# Make sure it's sorted\n",
    "date_2013.sort()\n",
    "# Walk through data\n",
    "locs_per_date = {}\n",
    "for day in date_2013:\n",
    "    locs = df_2013['Location'][(df_2013['Date'] == day) & (df_2013['Location'].isin(klocs) )].unique()\n",
    "    locs_per_date[day] = len(locs)\n",
    "x = locs_per_date.keys()\n",
    "y = locs_per_date.values()\n",
    "ax.scatter(x,y,label='2013')\n",
    "\n",
    "klocs = n_records_2014[n_records_2014['Location']['Count'] >= 153].index.get_level_values('Location').values\n",
    "# Make sure it's sorted\n",
    "date_2013.sort()\n",
    "# Walk through data\n",
    "locs_per_date = {}\n",
    "for day in date_2014:\n",
    "    locs = df_2014['Location'][(df_2014['Date'] == day) & (df_2014['Location'].isin(klocs) )].unique()\n",
    "    locs_per_date[day] = len(locs)\n",
    "x = locs_per_date.keys()\n",
    "y = locs_per_date.values()\n",
    "\n",
    "ax.scatter(x,y,color='g',label='2014')\n",
    "\n",
    "\n",
    "# Style panel\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.set_ylabel('Number of locations',fontsize=18,labelpad=20 )\n",
    "ax.yaxis.set_label_position(\"left\")\n",
    "# ax.set_xlim(185,0)\n",
    "ax.set_xlabel('Date',fontsize=18,labelpad=20 )\n",
    "ax.tick_params(labelsize=18)\n",
    "plt.legend();\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excluding all locations that have less than 13 (2013) or 14 (2014) records in the dataset removes the upwards scatter from the baseline in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[901  10   2  26   5   3   2   6   2   1   2   4   4  21   6   5   2  12]\n",
      "[186 185 184 183 178 174 169 153  13   9   8   7   6   5   4   3   2   1]\n"
     ]
    }
   ],
   "source": [
    "counts = n_records_2013['Location']['Count'].value_counts().sort_index(ascending=False).values\n",
    "levels = n_records_2013['Location']['Count'].value_counts().sort_index(ascending=False).index.values\n",
    "\n",
    "print(counts)\n",
    "print(levels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013:\n",
      "Excluded 263 rows (0.15%) and 59 locations (5.82%).\n"
     ]
    }
   ],
   "source": [
    "nrecords = 0\n",
    "nloc = 0\n",
    "limit = 153\n",
    "print('2013:')\n",
    "for x in np.arange(len(counts)):\n",
    "    if levels[x] < limit:\n",
    "        nrecords += levels[x]*counts[x]\n",
    "        nloc += counts[x]\n",
    "print('Excluded {} rows ({:4.2f}%) and {} locations ({:4.2f}%).'\n",
    "      .format(nrecords,nrecords*100/df_2013.shape[0],\n",
    "              nloc, nloc*100/len(df_2013['Location'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014:\n",
      "Excluded 344 rows (0.19%) and 53 locations (5.12%).\n"
     ]
    }
   ],
   "source": [
    "counts = n_records_2014['Location']['Count'].value_counts().sort_index(ascending=False).values\n",
    "levels = n_records_2014['Location']['Count'].value_counts().sort_index(ascending=False).index.values\n",
    "nrecords = 0\n",
    "nloc = 0\n",
    "limit = 153\n",
    "print('2014:')\n",
    "for x in np.arange(len(counts)):\n",
    "    if levels[x] < limit:\n",
    "        nrecords += levels[x]*counts[x]\n",
    "        nloc += counts[x]\n",
    "print('Excluded {} rows ({:4.2f}%) and {} locations ({:4.2f}%).'\n",
    "      .format(nrecords,nrecords*100/df_2014.shape[0],\n",
    "              nloc, nloc*100/len(df_2014['Location'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the geographical distribution of all locations that have less than 153 records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/NWrUoEWLFsycOZNmzZrRqFEjSpUqRbZs2UiePPlTZTRo0IBhw4bx/fffkyZN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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11785f210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(14, 20))\n",
    "\n",
    "mymap = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-80,\n",
    "                urcrnrlat=49, projection='lcc', lat_1=33, lat_2=45,\n",
    "                lon_0=-105, area_thresh=10000,\n",
    "                resolution = 'l',ax=ax[0])\n",
    "\n",
    "klocs = n_records_2013[n_records_2013['Location']['Count'] < 153].index.get_level_values('Location').values\n",
    "level = n_records_2013['Location']['Count'][n_records_2013['Location']['Count'] < 153].values\n",
    "# print(locations)\n",
    "lng = [x[0] for x in klocs]\n",
    "lat = [x[1] for x in klocs]\n",
    "yld = level\n",
    "x,y = mymap(lng, lat)\n",
    "\n",
    "im1 = mymap.scatter(x, y, c=yld, vmin=0, vmax=15, cmap=mpl.cm.get_cmap('rainbow'), zorder=2)\n",
    "\n",
    "mymap.drawparallels(np.arange(25,65,20))\n",
    "mymap.drawmeridians(np.arange(-120,-40,20))\n",
    "mymap.drawcoastlines()\n",
    "\n",
    "ax[0].annotate('2013', xy=(0.02, 1.02), fontsize=12,xycoords='axes fraction')\n",
    "\n",
    "# cax1 = fig.add_axes([0.92, 0.3, 0.03, 0.4])\n",
    "# cbar = plt.colorbar(im1,cax=cax1, orientation='vertical', extend='both', use_gridspec=True)\n",
    "# cbar.set_label('Length of Season')\n",
    "\n",
    "mymap = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-80,\n",
    "                urcrnrlat=49, projection='lcc', lat_1=33, lat_2=45,\n",
    "                lon_0=-105, area_thresh=10000,\n",
    "                resolution = 'l',ax=ax[1])\n",
    "\n",
    "klocs = n_records_2014[n_records_2014['Location']['Count'] < 153].index.get_level_values('Location').values\n",
    "level = n_records_2014['Location']['Count'][n_records_2014['Location']['Count'] < 153].values\n",
    "\n",
    "# print(locations)\n",
    "lng = [x[0] for x in klocs]\n",
    "lat = [x[1] for x in klocs]\n",
    "yld = level\n",
    "x,y = mymap(lng, lat)\n",
    "\n",
    "im2 = mymap.scatter(x, y, c=yld, vmin=0, vmax=15, cmap=mpl.cm.get_cmap('rainbow'), zorder=2)\n",
    "\n",
    "mymap.drawparallels(np.arange(25,65,20))\n",
    "mymap.drawmeridians(np.arange(-120,-40,20))\n",
    "mymap.drawcoastlines()\n",
    "\n",
    "ax[1].annotate('2014', xy=(0.02, 1.02), fontsize=12,xycoords='axes fraction')\n",
    "\n",
    "cax1 = fig.add_axes( [0.92, 0.3, 0.03, 0.4])\n",
    "cbar = plt.colorbar(im1,cax=cax1, orientation='vertical', extend='both', use_gridspec=True)\n",
    "cbar.set_label('Records in dataset')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the locations with only few records in 2013 and 2014:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 locations with low records are common between 2013 and 2014.\n"
     ]
    }
   ],
   "source": [
    "klocs_2013 = list(n_records_2013[n_records_2013['Location']['Count'] < 153]\n",
    "                  .index.get_level_values('Location').values)\n",
    "klocs_2014 = list(n_records_2014[n_records_2014['Location']['Count'] < 153]\n",
    "                  .index.get_level_values('Location').values)\n",
    "\n",
    "matches = set(klocs_2013) & set(klocs_2014)\n",
    "\n",
    "print('{} locations with low records are common between 2013 and 2014.'.format(len(matches)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the non-overlapping locations with few records there are two possibilities: either they are not included in the other year's data, or they have a large number of records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 6 17\n",
      "36 0 17\n"
     ]
    }
   ],
   "source": [
    "# n_records_2013 = df_2013.groupby(by='Location').agg({'Location': {'Count' : lambda x: x.count()}})\n",
    "\n",
    "\n",
    "# klocs = n_records_2013[n_records_2013['Location']['Count'] < 153].index.get_level_values('Location').values\n",
    "\n",
    "klocs_2013 = list(n_records_2013[n_records_2013['Location']['Count'] < 153]\n",
    "                  .index.get_level_values('Location').values)\n",
    "klocs_2014 = list(n_records_2014[n_records_2014['Location']['Count'] < 153]\n",
    "                  .index.get_level_values('Location').values)\n",
    "\n",
    "locs_2014 = list(df_2014['Location'].unique())\n",
    "matches = 0\n",
    "matches_highrec = 0\n",
    "non_matches = 0\n",
    "for loc in klocs_2013:\n",
    "    if loc in locs_2014:\n",
    "        if loc in klocs_2014:\n",
    "            matches += 1\n",
    "        else:\n",
    "            matches_highrec += 1\n",
    "    else:\n",
    "        non_matches += 1\n",
    "print(matches,matches_highrec,non_matches)\n",
    "\n",
    "locs_2013 = list(df_2013['Location'].unique())\n",
    "matches = 0\n",
    "matches_highrec = 0\n",
    "non_matches = 0\n",
    "for loc in klocs_2014:\n",
    "    if loc in locs_2013:\n",
    "        if loc in klocs_2013:\n",
    "            matches += 1\n",
    "        else:\n",
    "            matches_highrec += 1\n",
    "    else:\n",
    "        non_matches += 1\n",
    "print(matches,matches_highrec,non_matches)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I have show above, 36 locations with low record counts are common between both years. In addition, 17 low-record count locations from one year do not show up at all in the other year. And 6 low-record count locations from 2013 actually have high record counts in 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Precipitation'></a>\n",
    "## Precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    161454\n",
       "0.029        93\n",
       "0.026        87\n",
       "0.033        85\n",
       "0.019        85\n",
       "0.017        81\n",
       "0.034        80\n",
       "0.030        79\n",
       "0.028        78\n",
       "0.031        77\n",
       "Name: precipAccumulation, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013['precipAccumulation'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0000    124062\n",
       "0.0001      7344\n",
       "0.0002      4485\n",
       "0.0003      3124\n",
       "0.0004      2511\n",
       "0.0005      1962\n",
       "0.0006      1696\n",
       "0.0007      1442\n",
       "0.0008      1283\n",
       "0.0009      1176\n",
       "Name: precipIntensity, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013['precipIntensity'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On most days/locations it does not rain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The 'precipAccumulation' column aggregates the total precipitation per day. Now, calculate \n",
    "# the total cumulative sum. \n",
    "df_2013['precipTotal'] = df_2013.groupby(by='Location')['precipAccumulation'].apply(lambda x: x.cumsum())\n",
    "df_2014['precipTotal'] = df_2014.groupby(by='Location')['precipAccumulation'].apply(lambda x: x.cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-118.69523719999999, 46.811685799999992)      9.982  9.982\n",
      "(-118.35210929999998, 46.929839100000002)     12.312 12.312\n",
      "(-118.5101603, 47.006888099999998)            13.750 13.750\n",
      "(-118.6996774, 47.162341900000001)            12.934 12.934\n",
      "(-118.43405590000002, 47.157511999999997)     16.644 16.644\n"
     ]
    }
   ],
   "source": [
    "# Test that this worked \n",
    "locations = df_2013['Location'].unique()\n",
    "for idx,loc in enumerate(locations):\n",
    "    if idx < 5:\n",
    "        tmp1 = df_2013['precipAccumulation'][df_2013['Location'] == loc].sum()\n",
    "        tmp2 = df_2013['precipTotal'][df_2013['Location'] == loc].max()\n",
    "        print('{:45} {:6.3f} {:6.3f}'.format(loc, tmp1, tmp2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, that seem to have worked!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yield</th>\n",
       "      <th>precipTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Yield</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.098991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTotal</th>\n",
       "      <td>0.098991</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Yield  precipTotal\n",
       "Yield        1.000000     0.098991\n",
       "precipTotal  0.098991     1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013.groupby(by='Location').agg({'precipTotal': np.max, 'Yield': np.max}).corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation between final yield and total amount of precipitation is not too great. But yield is a county average while precipTotal is for individual locations.\n",
    "\n",
    "Let's try this again on a county basis and averaging the total precipitation values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yield</th>\n",
       "      <th>precipTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Yield</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTotal</th>\n",
       "      <td>0.1178</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Yield  precipTotal\n",
       "Yield        1.0000       0.1178\n",
       "precipTotal  0.1178       1.0000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013.groupby(by='CountyName').agg({'precipTotal': np.mean, 'Yield': np.max}).corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the correlation is a bit stronger, but still not great. Let's see how it looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10d30dd90>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e68490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df_2013.groupby(by='CountyName').agg({'precipTotal': np.mean, 'Yield': np.max})\n",
    "x = tmp['precipTotal'].values\n",
    "y = tmp['Yield'].values\n",
    "\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Temperature'></a>\n",
    "## Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if I can generate something akin to 'accumulated heat':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Difference and ratio between daily min and max temperatures\n",
    "df_2013['temperatureDiff'] = df_2013['temperatureMax'] - df_2013['temperatureMin']\n",
    "df_2013['temperatureRatio'] = df_2013['temperatureMax'] / df_2013['temperatureMin']\n",
    "#\n",
    "df_2014['temperatureDiff'] = df_2014['temperatureMax'] - df_2014['temperatureMin']\n",
    "df_2014['temperatureRatio'] = df_2014['temperatureMax'] / df_2014['temperatureMin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Daily average temperature as average between min and max\n",
    "df_2013['temperatureAverage'] = (df_2013['temperatureMax'] + df_2013['temperatureMin']) / 2.\n",
    "df_2014['temperatureAverage'] = (df_2014['temperatureMax'] + df_2014['temperatureMin']) / 2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountyName</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>windSpeed</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>DayInSeason</th>\n",
       "      <th>Yield</th>\n",
       "      <th>Location</th>\n",
       "      <th>precipTotal</th>\n",
       "      <th>temperatureDiff</th>\n",
       "      <th>temperatureRatio</th>\n",
       "      <th>temperatureAverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>46.811686</td>\n",
       "      <td>-118.695237</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>35.70</td>\n",
       "      <td>20.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>...</td>\n",
       "      <td>214</td>\n",
       "      <td>1.18</td>\n",
       "      <td>134.110657</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>(-118.6952372, 46.8116858)</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.22</td>\n",
       "      <td>1.299127</td>\n",
       "      <td>31.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>46.929839</td>\n",
       "      <td>-118.352109</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>35.10</td>\n",
       "      <td>26.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.77</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>1.01</td>\n",
       "      <td>131.506592</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>(-118.3521093, 46.9298391)</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.18</td>\n",
       "      <td>1.303863</td>\n",
       "      <td>31.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.006888</td>\n",
       "      <td>-118.510160</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>33.38</td>\n",
       "      <td>26.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.36</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>158</td>\n",
       "      <td>1.03</td>\n",
       "      <td>131.472946</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>(-118.5101603, 47.0068881)</td>\n",
       "      <td>0.020</td>\n",
       "      <td>6.43</td>\n",
       "      <td>1.238590</td>\n",
       "      <td>30.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.162342</td>\n",
       "      <td>-118.699677</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>28.05</td>\n",
       "      <td>25.93</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.47</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>153</td>\n",
       "      <td>1.84</td>\n",
       "      <td>131.288300</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>(-118.6996774, 47.1623419)</td>\n",
       "      <td>0.036</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1.221568</td>\n",
       "      <td>30.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Washington</td>\n",
       "      <td>47.157512</td>\n",
       "      <td>-118.434056</td>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>28.83</td>\n",
       "      <td>25.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>1.85</td>\n",
       "      <td>131.288300</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>(-118.4340559, 47.157512)</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.78</td>\n",
       "      <td>1.250462</td>\n",
       "      <td>30.460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountyName       State   Latitude   Longitude       Date  \\\n",
       "0      Adams  Washington  46.811686 -118.695237 2013-11-30   \n",
       "1      Adams  Washington  46.929839 -118.352109 2013-11-30   \n",
       "2      Adams  Washington  47.006888 -118.510160 2013-11-30   \n",
       "3      Adams  Washington  47.162342 -118.699677 2013-11-30   \n",
       "4      Adams  Washington  47.157512 -118.434056 2013-11-30   \n",
       "\n",
       "   apparentTemperatureMax  apparentTemperatureMin  cloudCover  dewPoint  \\\n",
       "0                   35.70                   20.85        0.00     29.53   \n",
       "1                   35.10                   26.92        0.00     29.77   \n",
       "2                   33.38                   26.95        0.00     29.36   \n",
       "3                   28.05                   25.93        0.91     29.47   \n",
       "4                   28.83                   25.98        0.91     29.86   \n",
       "\n",
       "   humidity         ...          windBearing  windSpeed        NDVI  \\\n",
       "0      0.91         ...                  214       1.18  134.110657   \n",
       "1      0.93         ...                  166       1.01  131.506592   \n",
       "2      0.94         ...                  158       1.03  131.472946   \n",
       "3      0.94         ...                  153       1.84  131.288300   \n",
       "4      0.94         ...                  156       1.85  131.288300   \n",
       "\n",
       "   DayInSeason  Yield                    Location  precipTotal  \\\n",
       "0            0   35.7  (-118.6952372, 46.8116858)        0.000   \n",
       "1            0   35.7  (-118.3521093, 46.9298391)        0.000   \n",
       "2            0   35.7  (-118.5101603, 47.0068881)        0.020   \n",
       "3            0   35.7  (-118.6996774, 47.1623419)        0.036   \n",
       "4            0   35.7   (-118.4340559, 47.157512)        0.000   \n",
       "\n",
       "   temperatureDiff  temperatureRatio  temperatureAverage  \n",
       "0             8.22          1.299127              31.590  \n",
       "1             8.18          1.303863              31.010  \n",
       "2             6.43          1.238590              30.165  \n",
       "3             6.02          1.221568              30.180  \n",
       "4             6.78          1.250462              30.460  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Time series'></a>\n",
    "## Time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step I will explore the variations of feature values over time for a rabdom location. This may help to identify additonal features that can be engineered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get a random location out of the location set that has the longest season data on record\n",
    "locations = (gb_agg_2013[gb_agg_2013['DayInSeason']['Length_of_Season'] == 185]\n",
    "             .index.get_level_values('Location').values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set the seed for reproducability\n",
    "np.random.seed(seed=1234)\n",
    "# Randomly choose one entry from the locations subset\n",
    "idx = np.random.choice(len(locations),1)\n",
    "location = locations[idx][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-96.274679599999999, 33.745539299999997)\n"
     ]
    }
   ],
   "source": [
    "print(location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "186\n",
      "186\n"
     ]
    }
   ],
   "source": [
    "dates = df_2013['Date'][df_2013['Location'] == location]\n",
    "dis = df_2013['DayInSeason'][df_2013['Location'] == location]\n",
    "print(len(dates.unique()))\n",
    "print(len(dis.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_to_plot = ['temperatureMax','temperatureMin','temperatureDiff', 'temperatureRatio',\n",
    "                    'temperatureAverage', 'NDVI',\n",
    "                    'Yield','precipIntensity','precipTotal','pressure','visibility','windSpeed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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S1XD48BacOLFL73sQBEEQhOR4+pQH0Rs2cHhW06bGt42LYyNj0yb2sFSpkrBu3DjO6xkx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9/mq3RS6HSiVL0OPTRcPIitZZXK//4XAqjE1KZMzSt64YWQwpfLk7fu3cPvovdb9sSn0OlY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\nqspNNz0OwOrVi+nbtz0NGjSjX7+XqVixGr/88gNbtqzZv38RyVSm8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0kAItIcPxbcgSsF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\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5Gj2a99X585FpV6CkpjL0/rrr7K0/dCjDc8PpDdu/n6HImtxNMOpzPUWktIgUFJHyIvKJ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\nsCZFAADAWgnI5HQQ+ysBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d80c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_plt = len(features_to_plot)\n",
    "fig, ax = plt.subplots(n_plt,1,figsize=(14, 20))\n",
    "\n",
    "for i in np.arange(n_plt):\n",
    "    y = df_2013[features_to_plot[i]][df_2013['Location'] == location]\n",
    "    ax[i].plot(dis,y)\n",
    "    ax[i].text(0.05, 0.9, features_to_plot[i],\n",
    "               verticalalignment='top', horizontalalignment='left',\n",
    "               transform=ax[i].transAxes,\n",
    "               color='black', fontsize=15,\n",
    "               bbox={'facecolor':'blue', 'alpha':0.25, 'pad':5})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "There is quite some interesting structure in the data, especially for the temperature-related features (flat during the first half of the season with a general upwards trend in the second half) and the NDVI. I may be able to take advantage of this during the feature engineering phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Basic statistics'></a>\n",
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>missing %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>37.525355</td>\n",
       "      <td>4.918148</td>\n",
       "      <td>27.803015</td>\n",
       "      <td>34.135464</td>\n",
       "      <td>36.810364</td>\n",
       "      <td>38.945878</td>\n",
       "      <td>48.981928</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>-100.877849</td>\n",
       "      <td>5.960510</td>\n",
       "      <td>-120.914093</td>\n",
       "      <td>-101.294945</td>\n",
       "      <td>-99.128028</td>\n",
       "      <td>-97.352044</td>\n",
       "      <td>-94.613571</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>54.836240</td>\n",
       "      <td>23.827278</td>\n",
       "      <td>-39.970000</td>\n",
       "      <td>37.830000</td>\n",
       "      <td>58.880000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>177.320000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>27.915526</td>\n",
       "      <td>22.029381</td>\n",
       "      <td>-58.420000</td>\n",
       "      <td>14.310000</td>\n",
       "      <td>26.560000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>77.180000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudCover</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.071478</td>\n",
       "      <td>0.138932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dewPoint</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>29.712311</td>\n",
       "      <td>16.687710</td>\n",
       "      <td>-36.090000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>27.850000</td>\n",
       "      <td>38.890000</td>\n",
       "      <td>75.180000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.593973</td>\n",
       "      <td>0.163590</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensity</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.004559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.152900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.044782</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>2.054900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipProbability</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.133540</td>\n",
       "      <td>0.253415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipAccumulation</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.057473</td>\n",
       "      <td>0.362080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.487000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsRain</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.210667</td>\n",
       "      <td>0.407784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsSnow</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.090370</td>\n",
       "      <td>0.286712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsOther</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pressure</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>1017.075279</td>\n",
       "      <td>8.611562</td>\n",
       "      <td>942.490000</td>\n",
       "      <td>1011.230000</td>\n",
       "      <td>1016.690000</td>\n",
       "      <td>1022.910000</td>\n",
       "      <td>1048.130000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMax</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>57.547801</td>\n",
       "      <td>20.755273</td>\n",
       "      <td>-22.000000</td>\n",
       "      <td>43.350000</td>\n",
       "      <td>58.880000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>105.200000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMin</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>34.393113</td>\n",
       "      <td>17.801718</td>\n",
       "      <td>-39.790000</td>\n",
       "      <td>23.420000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>46.070000</td>\n",
       "      <td>77.180000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>9.285889</td>\n",
       "      <td>1.281698</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>9.890000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windBearing</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>191.165483</td>\n",
       "      <td>104.103600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windSpeed</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>8.436830</td>\n",
       "      <td>4.767758</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>4.760000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>11.530000</td>\n",
       "      <td>31.730000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NDVI</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>146.346701</td>\n",
       "      <td>10.130267</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>137.876236</td>\n",
       "      <td>147.225510</td>\n",
       "      <td>152.935913</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayInSeason</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>92.633084</td>\n",
       "      <td>53.608768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yield</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>31.443024</td>\n",
       "      <td>15.214107</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>72.200000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTotal</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>7.529662</td>\n",
       "      <td>9.755143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.053000</td>\n",
       "      <td>4.728000</td>\n",
       "      <td>8.594000</td>\n",
       "      <td>135.177000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureDiff</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>23.154689</td>\n",
       "      <td>9.605510</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>16.040000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>78.960000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureRatio</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1877.500000</td>\n",
       "      <td>1.402267</td>\n",
       "      <td>1.645285</td>\n",
       "      <td>2.032579</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureAverage</th>\n",
       "      <td>177493.0</td>\n",
       "      <td>45.970457</td>\n",
       "      <td>18.728983</td>\n",
       "      <td>-30.500000</td>\n",
       "      <td>33.535000</td>\n",
       "      <td>46.190000</td>\n",
       "      <td>59.280000</td>\n",
       "      <td>87.830000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count         mean         std          min  \\\n",
       "Latitude                177493.0    37.525355    4.918148    27.803015   \n",
       "Longitude               177493.0  -100.877849    5.960510  -120.914093   \n",
       "apparentTemperatureMax  177493.0    54.836240   23.827278   -39.970000   \n",
       "apparentTemperatureMin  177493.0    27.915526   22.029381   -58.420000   \n",
       "cloudCover              177493.0     0.071478    0.138932     0.000000   \n",
       "dewPoint                177493.0    29.712311   16.687710   -36.090000   \n",
       "humidity                177493.0     0.593973    0.163590     0.080000   \n",
       "precipIntensity         177493.0     0.001158    0.004559     0.000000   \n",
       "precipIntensityMax      177493.0     0.010633    0.044782     0.000000   \n",
       "precipProbability       177493.0     0.133540    0.253415     0.000000   \n",
       "precipAccumulation      177493.0     0.057473    0.362080     0.000000   \n",
       "precipTypeIsRain        177493.0     0.210667    0.407784     0.000000   \n",
       "precipTypeIsSnow        177493.0     0.090370    0.286712     0.000000   \n",
       "precipTypeIsOther       177493.0     0.000000    0.000000     0.000000   \n",
       "pressure                177493.0  1017.075279    8.611562   942.490000   \n",
       "temperatureMax          177493.0    57.547801   20.755273   -22.000000   \n",
       "temperatureMin          177493.0    34.393113   17.801718   -39.790000   \n",
       "visibility              177493.0     9.285889    1.281698     0.600000   \n",
       "windBearing             177493.0   191.165483  104.103600     0.000000   \n",
       "windSpeed               177493.0     8.436830    4.767758     0.040000   \n",
       "NDVI                    177493.0   146.346701   10.130267   117.000000   \n",
       "DayInSeason             177493.0    92.633084   53.608768     0.000000   \n",
       "Yield                   177493.0    31.443024   15.214107     9.000000   \n",
       "precipTotal             177493.0     7.529662    9.755143     0.000000   \n",
       "temperatureDiff         177493.0    23.154689    9.605510     1.100000   \n",
       "temperatureRatio        177493.0          inf         NaN -1877.500000   \n",
       "temperatureAverage      177493.0    45.970457   18.728983   -30.500000   \n",
       "\n",
       "                                25%          50%          75%          max  \\\n",
       "Latitude                  34.135464    36.810364    38.945878    48.981928   \n",
       "Longitude               -101.294945   -99.128028   -97.352044   -94.613571   \n",
       "apparentTemperatureMax    37.830000    58.880000    73.100000   177.320000   \n",
       "apparentTemperatureMin    14.310000    26.560000    42.200000    77.180000   \n",
       "cloudCover                 0.000000     0.010000     0.090000     1.000000   \n",
       "dewPoint                  19.600000    27.850000    38.890000    75.180000   \n",
       "humidity                   0.470000     0.600000     0.720000     1.000000   \n",
       "precipIntensity            0.000000     0.000000     0.000200     0.152900   \n",
       "precipIntensityMax         0.000000     0.000000     0.002800     2.054900   \n",
       "precipProbability          0.000000     0.000000     0.090000     0.960000   \n",
       "precipAccumulation         0.000000     0.000000     0.000000    19.487000   \n",
       "precipTypeIsRain           0.000000     0.000000     0.000000     1.000000   \n",
       "precipTypeIsSnow           0.000000     0.000000     0.000000     1.000000   \n",
       "precipTypeIsOther          0.000000     0.000000     0.000000     0.000000   \n",
       "pressure                1011.230000  1016.690000  1022.910000  1048.130000   \n",
       "temperatureMax            43.350000    58.880000    73.100000   105.200000   \n",
       "temperatureMin            23.420000    33.250000    46.070000    77.180000   \n",
       "visibility                 9.180000     9.890000    10.000000    10.000000   \n",
       "windBearing              127.000000   192.000000   275.000000   359.000000   \n",
       "windSpeed                  4.760000     7.670000    11.530000    31.730000   \n",
       "NDVI                     137.876236   147.225510   152.935913   206.000000   \n",
       "DayInSeason               46.000000    93.000000   139.000000   185.000000   \n",
       "Yield                     17.300000    31.100000    43.100000    72.200000   \n",
       "precipTotal                2.053000     4.728000     8.594000   135.177000   \n",
       "temperatureDiff           16.040000    22.700000    29.990000    78.960000   \n",
       "temperatureRatio           1.402267     1.645285     2.032579          inf   \n",
       "temperatureAverage        33.535000    46.190000    59.280000    87.830000   \n",
       "\n",
       "                        missing %  \n",
       "Latitude                      0.0  \n",
       "Longitude                     0.0  \n",
       "apparentTemperatureMax        0.0  \n",
       "apparentTemperatureMin        0.0  \n",
       "cloudCover                    0.0  \n",
       "dewPoint                      0.0  \n",
       "humidity                      0.0  \n",
       "precipIntensity               0.0  \n",
       "precipIntensityMax            0.0  \n",
       "precipProbability             0.0  \n",
       "precipAccumulation            0.0  \n",
       "precipTypeIsRain              0.0  \n",
       "precipTypeIsSnow              0.0  \n",
       "precipTypeIsOther             0.0  \n",
       "pressure                      0.0  \n",
       "temperatureMax                0.0  \n",
       "temperatureMin                0.0  \n",
       "visibility                    0.0  \n",
       "windBearing                   0.0  \n",
       "windSpeed                     0.0  \n",
       "NDVI                          0.0  \n",
       "DayInSeason                   0.0  \n",
       "Yield                         0.0  \n",
       "precipTotal                   0.0  \n",
       "temperatureDiff               0.0  \n",
       "temperatureRatio              0.0  \n",
       "temperatureAverage            0.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_2013.describe().T\n",
    "X['missing %'] = 1 - (X['count'] / len(df_2013))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>missing %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>36.976937</td>\n",
       "      <td>4.926798</td>\n",
       "      <td>27.363741</td>\n",
       "      <td>33.598030</td>\n",
       "      <td>36.164529</td>\n",
       "      <td>38.451645</td>\n",
       "      <td>48.981928</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>-100.547088</td>\n",
       "      <td>5.587446</td>\n",
       "      <td>-120.914093</td>\n",
       "      <td>-101.174610</td>\n",
       "      <td>-99.068063</td>\n",
       "      <td>-97.270576</td>\n",
       "      <td>-94.164602</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>57.146379</td>\n",
       "      <td>20.217840</td>\n",
       "      <td>-24.340000</td>\n",
       "      <td>41.390000</td>\n",
       "      <td>61.460000</td>\n",
       "      <td>72.990000</td>\n",
       "      <td>103.910000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>33.669343</td>\n",
       "      <td>19.649074</td>\n",
       "      <td>-43.250000</td>\n",
       "      <td>20.890000</td>\n",
       "      <td>33.310000</td>\n",
       "      <td>50.630000</td>\n",
       "      <td>79.560000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudCover</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.131278</td>\n",
       "      <td>0.170087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dewPoint</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>36.664752</td>\n",
       "      <td>16.202934</td>\n",
       "      <td>-21.950000</td>\n",
       "      <td>25.740000</td>\n",
       "      <td>35.130000</td>\n",
       "      <td>48.840000</td>\n",
       "      <td>78.350000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.680338</td>\n",
       "      <td>0.150459</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensity</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.002501</td>\n",
       "      <td>0.007232</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.255800</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.021390</td>\n",
       "      <td>0.071354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010100</td>\n",
       "      <td>3.367400</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipProbability</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.205662</td>\n",
       "      <td>0.307787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipAccumulation</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.028738</td>\n",
       "      <td>0.207037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.063000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsRain</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.336364</td>\n",
       "      <td>0.472467</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsSnow</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.062537</td>\n",
       "      <td>0.242128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTypeIsOther</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pressure</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>1018.782445</td>\n",
       "      <td>7.751215</td>\n",
       "      <td>945.090000</td>\n",
       "      <td>1013.190000</td>\n",
       "      <td>1018.300000</td>\n",
       "      <td>1023.750000</td>\n",
       "      <td>1049.530000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMax</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>58.989567</td>\n",
       "      <td>17.404119</td>\n",
       "      <td>-5.570000</td>\n",
       "      <td>45.850000</td>\n",
       "      <td>61.460000</td>\n",
       "      <td>72.990000</td>\n",
       "      <td>95.580000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMin</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>38.757876</td>\n",
       "      <td>15.739407</td>\n",
       "      <td>-24.960000</td>\n",
       "      <td>28.390000</td>\n",
       "      <td>38.510000</td>\n",
       "      <td>50.630000</td>\n",
       "      <td>79.560000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>9.042096</td>\n",
       "      <td>1.473604</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>8.690000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windBearing</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>171.727963</td>\n",
       "      <td>102.727345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windSpeed</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>7.595578</td>\n",
       "      <td>4.072584</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>7.090000</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>30.460000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NDVI</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>145.885753</td>\n",
       "      <td>10.335488</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>136.480255</td>\n",
       "      <td>146.507538</td>\n",
       "      <td>153.231476</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayInSeason</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>92.652860</td>\n",
       "      <td>53.633300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yield</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>33.836357</td>\n",
       "      <td>10.630116</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipTotal</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>3.542021</td>\n",
       "      <td>5.423150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>2.023000</td>\n",
       "      <td>4.663000</td>\n",
       "      <td>96.409000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureDiff</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>20.231691</td>\n",
       "      <td>9.561771</td>\n",
       "      <td>1.220000</td>\n",
       "      <td>12.840000</td>\n",
       "      <td>19.420000</td>\n",
       "      <td>26.960000</td>\n",
       "      <td>54.510000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureRatio</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3467.000000</td>\n",
       "      <td>1.288352</td>\n",
       "      <td>1.500663</td>\n",
       "      <td>1.832792</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureAverage</th>\n",
       "      <td>182549.0</td>\n",
       "      <td>48.873721</td>\n",
       "      <td>15.888967</td>\n",
       "      <td>-12.725000</td>\n",
       "      <td>37.630000</td>\n",
       "      <td>50.105000</td>\n",
       "      <td>61.050000</td>\n",
       "      <td>85.185000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count         mean         std          min  \\\n",
       "Latitude                182549.0    36.976937    4.926798    27.363741   \n",
       "Longitude               182549.0  -100.547088    5.587446  -120.914093   \n",
       "apparentTemperatureMax  182549.0    57.146379   20.217840   -24.340000   \n",
       "apparentTemperatureMin  182549.0    33.669343   19.649074   -43.250000   \n",
       "cloudCover              182549.0     0.131278    0.170087     0.000000   \n",
       "dewPoint                182549.0    36.664752   16.202934   -21.950000   \n",
       "humidity                182549.0     0.680338    0.150459     0.140000   \n",
       "precipIntensity         182549.0     0.002501    0.007232     0.000000   \n",
       "precipIntensityMax      182549.0     0.021390    0.071354     0.000000   \n",
       "precipProbability       182549.0     0.205662    0.307787     0.000000   \n",
       "precipAccumulation      182549.0     0.028738    0.207037     0.000000   \n",
       "precipTypeIsRain        182549.0     0.336364    0.472467     0.000000   \n",
       "precipTypeIsSnow        182549.0     0.062537    0.242128     0.000000   \n",
       "precipTypeIsOther       182549.0     0.000000    0.000000     0.000000   \n",
       "pressure                182549.0  1018.782445    7.751215   945.090000   \n",
       "temperatureMax          182549.0    58.989567   17.404119    -5.570000   \n",
       "temperatureMin          182549.0    38.757876   15.739407   -24.960000   \n",
       "visibility              182549.0     9.042096    1.473604     0.460000   \n",
       "windBearing             182549.0   171.727963  102.727345     0.000000   \n",
       "windSpeed               182549.0     7.595578    4.072584     0.020000   \n",
       "NDVI                    182549.0   145.885753   10.335488   101.000000   \n",
       "DayInSeason             182549.0    92.652860   53.633300     0.000000   \n",
       "Yield                   182549.0    33.836357   10.630116    12.900000   \n",
       "precipTotal             182549.0     3.542021    5.423150     0.000000   \n",
       "temperatureDiff         182549.0    20.231691    9.561771     1.220000   \n",
       "temperatureRatio        182549.0          inf         NaN -3467.000000   \n",
       "temperatureAverage      182549.0    48.873721   15.888967   -12.725000   \n",
       "\n",
       "                                25%          50%          75%          max  \\\n",
       "Latitude                  33.598030    36.164529    38.451645    48.981928   \n",
       "Longitude               -101.174610   -99.068063   -97.270576   -94.164602   \n",
       "apparentTemperatureMax    41.390000    61.460000    72.990000   103.910000   \n",
       "apparentTemperatureMin    20.890000    33.310000    50.630000    79.560000   \n",
       "cloudCover                 0.010000     0.080000     0.180000     1.000000   \n",
       "dewPoint                  25.740000    35.130000    48.840000    78.350000   \n",
       "humidity                   0.570000     0.680000     0.800000     1.000000   \n",
       "precipIntensity            0.000000     0.000000     0.001000     0.255800   \n",
       "precipIntensityMax         0.000000     0.000000     0.010100     3.367400   \n",
       "precipProbability          0.000000     0.000000     0.510000     0.970000   \n",
       "precipAccumulation         0.000000     0.000000     0.000000    17.063000   \n",
       "precipTypeIsRain           0.000000     0.000000     1.000000     1.000000   \n",
       "precipTypeIsSnow           0.000000     0.000000     0.000000     1.000000   \n",
       "precipTypeIsOther          0.000000     0.000000     0.000000     0.000000   \n",
       "pressure                1013.190000  1018.300000  1023.750000  1049.530000   \n",
       "temperatureMax            45.850000    61.460000    72.990000    95.580000   \n",
       "temperatureMin            28.390000    38.510000    50.630000    79.560000   \n",
       "visibility                 8.690000     9.750000     9.990000    10.000000   \n",
       "windBearing               89.000000   171.000000   246.000000   359.000000   \n",
       "windSpeed                  4.490000     7.090000    10.150000    30.460000   \n",
       "NDVI                     136.480255   146.507538   153.231476   208.000000   \n",
       "DayInSeason               46.000000    93.000000   139.000000   185.000000   \n",
       "Yield                     25.700000    32.900000    41.500000    78.000000   \n",
       "precipTotal                0.249000     2.023000     4.663000    96.409000   \n",
       "temperatureDiff           12.840000    19.420000    26.960000    54.510000   \n",
       "temperatureRatio           1.288352     1.500663     1.832792          inf   \n",
       "temperatureAverage        37.630000    50.105000    61.050000    85.185000   \n",
       "\n",
       "                        missing %  \n",
       "Latitude                      0.0  \n",
       "Longitude                     0.0  \n",
       "apparentTemperatureMax        0.0  \n",
       "apparentTemperatureMin        0.0  \n",
       "cloudCover                    0.0  \n",
       "dewPoint                      0.0  \n",
       "humidity                      0.0  \n",
       "precipIntensity               0.0  \n",
       "precipIntensityMax            0.0  \n",
       "precipProbability             0.0  \n",
       "precipAccumulation            0.0  \n",
       "precipTypeIsRain              0.0  \n",
       "precipTypeIsSnow              0.0  \n",
       "precipTypeIsOther             0.0  \n",
       "pressure                      0.0  \n",
       "temperatureMax                0.0  \n",
       "temperatureMin                0.0  \n",
       "visibility                    0.0  \n",
       "windBearing                   0.0  \n",
       "windSpeed                     0.0  \n",
       "NDVI                          0.0  \n",
       "DayInSeason                   0.0  \n",
       "Yield                         0.0  \n",
       "precipTotal                   0.0  \n",
       "temperatureDiff               0.0  \n",
       "temperatureRatio              0.0  \n",
       "temperatureAverage            0.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_2014.describe().T\n",
    "X['missing %'] = 1 - (X['count'] / len(df_2014))\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both years, the (binary) column 'precipTypeIsOther' is always zero (zero std and zero min/max). This feature can be safely dropped as it holds no predictive power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Correlations'></a>\n",
    "## Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairwise correlations between different features can help to eliminate features from modeling. Adding highly correlated features can dilute the predictive power of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Find all column pairs with correlation coefficients above a certain threshold\n",
    "X = df_2013.corr()\n",
    "limit = 0.65\n",
    "index = X.index.tolist()\n",
    "n = len(index)\n",
    "newDF = pd.DataFrame(columns=['col1','col2','corr','corr col1 with target', 'corr col2 with target'])\n",
    "pos = 0\n",
    "for nidx,idx in enumerate(index):\n",
    "    for k in range(n):\n",
    "        if (k > nidx):\n",
    "            value = X[idx].iloc[k]\n",
    "        else:\n",
    "            value = 0.0\n",
    "        if value >= limit:\n",
    "            corr_col1 = X['Yield'].loc[idx]\n",
    "            corr_col2 = X['Yield'].loc[index[k]]\n",
    "            newDF.loc[pos] = [idx, index[k] , value, corr_col1, corr_col2]\n",
    "            pos += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>corr</th>\n",
       "      <th>corr col1 with target</th>\n",
       "      <th>corr col2 with target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.992737</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>-0.060512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.991843</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.152702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.975612</td>\n",
       "      <td>-0.152702</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.967973</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.966851</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.966698</td>\n",
       "      <td>-0.060512</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>precipIntensity</td>\n",
       "      <td>precipIntensityMax</td>\n",
       "      <td>0.912594</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.030030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.910333</td>\n",
       "      <td>0.011607</td>\n",
       "      <td>-0.060512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>dewPoint</td>\n",
       "      <td>0.909563</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>0.011607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.893452</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>-0.152702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>0.888862</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.049998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.886946</td>\n",
       "      <td>-0.152702</td>\n",
       "      <td>-0.060512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.880383</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.060512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.871677</td>\n",
       "      <td>0.011607</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>dewPoint</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>0.011607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.792366</td>\n",
       "      <td>0.011607</td>\n",
       "      <td>-0.152702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.784945</td>\n",
       "      <td>-0.152702</td>\n",
       "      <td>-0.170673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.784701</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.170673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.755626</td>\n",
       "      <td>-0.170673</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>precipProbability</td>\n",
       "      <td>precipTypeIsRain</td>\n",
       "      <td>0.709446</td>\n",
       "      <td>0.111940</td>\n",
       "      <td>0.108773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.684965</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>-0.170673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>DayInSeason</td>\n",
       "      <td>0.684064</td>\n",
       "      <td>-0.060512</td>\n",
       "      <td>-0.001171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>DayInSeason</td>\n",
       "      <td>0.678918</td>\n",
       "      <td>-0.049998</td>\n",
       "      <td>-0.001171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DayInSeason</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.678017</td>\n",
       "      <td>-0.001171</td>\n",
       "      <td>-0.113369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.674792</td>\n",
       "      <td>-0.060512</td>\n",
       "      <td>-0.170673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      col1                    col2      corr  \\\n",
       "8   apparentTemperatureMin          temperatureMin  0.992737   \n",
       "2   apparentTemperatureMax          temperatureMax  0.991843   \n",
       "19          temperatureMax      temperatureAverage  0.975612   \n",
       "5   apparentTemperatureMax      temperatureAverage  0.967973   \n",
       "11  apparentTemperatureMin      temperatureAverage  0.966851   \n",
       "22          temperatureMin      temperatureAverage  0.966698   \n",
       "15         precipIntensity      precipIntensityMax  0.912594   \n",
       "13                dewPoint          temperatureMin  0.910333   \n",
       "6   apparentTemperatureMin                dewPoint  0.909563   \n",
       "7   apparentTemperatureMin          temperatureMax  0.893452   \n",
       "0   apparentTemperatureMax  apparentTemperatureMin  0.888862   \n",
       "17          temperatureMax          temperatureMin  0.886946   \n",
       "3   apparentTemperatureMax          temperatureMin  0.880383   \n",
       "14                dewPoint      temperatureAverage  0.871677   \n",
       "1   apparentTemperatureMax                dewPoint  0.796342   \n",
       "12                dewPoint          temperatureMax  0.792366   \n",
       "18          temperatureMax                    NDVI  0.784945   \n",
       "4   apparentTemperatureMax                    NDVI  0.784701   \n",
       "23                    NDVI      temperatureAverage  0.755626   \n",
       "16       precipProbability        precipTypeIsRain  0.709446   \n",
       "9   apparentTemperatureMin                    NDVI  0.684965   \n",
       "21          temperatureMin             DayInSeason  0.684064   \n",
       "10  apparentTemperatureMin             DayInSeason  0.678918   \n",
       "24             DayInSeason      temperatureAverage  0.678017   \n",
       "20          temperatureMin                    NDVI  0.674792   \n",
       "\n",
       "    corr col1 with target  corr col2 with target  \n",
       "8               -0.049998              -0.060512  \n",
       "2               -0.140315              -0.152702  \n",
       "19              -0.152702              -0.113369  \n",
       "5               -0.140315              -0.113369  \n",
       "11              -0.049998              -0.113369  \n",
       "22              -0.060512              -0.113369  \n",
       "15               0.046476               0.030030  \n",
       "13               0.011607              -0.060512  \n",
       "6               -0.049998               0.011607  \n",
       "7               -0.049998              -0.152702  \n",
       "0               -0.140315              -0.049998  \n",
       "17              -0.152702              -0.060512  \n",
       "3               -0.140315              -0.060512  \n",
       "14               0.011607              -0.113369  \n",
       "1               -0.140315               0.011607  \n",
       "12               0.011607              -0.152702  \n",
       "18              -0.152702              -0.170673  \n",
       "4               -0.140315              -0.170673  \n",
       "23              -0.170673              -0.113369  \n",
       "16               0.111940               0.108773  \n",
       "9               -0.049998              -0.170673  \n",
       "21              -0.060512              -0.001171  \n",
       "10              -0.049998              -0.001171  \n",
       "24              -0.001171              -0.113369  \n",
       "20              -0.060512              -0.170673  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newDF.sort_values(by='corr', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find all column pairs with correlation coefficients above a certain threshold\n",
    "X = df_2014.corr()\n",
    "limit = 0.65\n",
    "index = X.index.tolist()\n",
    "n = len(index)\n",
    "newDF = pd.DataFrame(columns=['col1','col2','corr','corr col1 with target', 'corr col2 with target'])\n",
    "pos = 0\n",
    "for nidx,idx in enumerate(index):\n",
    "    for k in range(n):\n",
    "        if (k > nidx):\n",
    "            value = X[idx].iloc[k]\n",
    "        else:\n",
    "            value = 0.0\n",
    "        if value >= limit:\n",
    "            corr_col1 = X['Yield'].loc[idx]\n",
    "            corr_col2 = X['Yield'].loc[index[k]]\n",
    "            newDF.loc[pos] = [idx, index[k] , value, corr_col1, corr_col2]\n",
    "            pos += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>corr</th>\n",
       "      <th>corr col1 with target</th>\n",
       "      <th>corr col2 with target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.993237</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.172160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.992886</td>\n",
       "      <td>-0.146381</td>\n",
       "      <td>-0.153430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.962823</td>\n",
       "      <td>-0.172160</td>\n",
       "      <td>-0.170282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.960737</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.170282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.954348</td>\n",
       "      <td>-0.146381</td>\n",
       "      <td>-0.170282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.954346</td>\n",
       "      <td>-0.153430</td>\n",
       "      <td>-0.170282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.951772</td>\n",
       "      <td>-0.158787</td>\n",
       "      <td>-0.153430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>dewPoint</td>\n",
       "      <td>0.949988</td>\n",
       "      <td>-0.146381</td>\n",
       "      <td>-0.158787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureAverage</td>\n",
       "      <td>0.903411</td>\n",
       "      <td>-0.158787</td>\n",
       "      <td>-0.170282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>precipIntensity</td>\n",
       "      <td>precipIntensityMax</td>\n",
       "      <td>0.863038</td>\n",
       "      <td>-0.015459</td>\n",
       "      <td>-0.020094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>0.849104</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.146381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.844614</td>\n",
       "      <td>-0.146381</td>\n",
       "      <td>-0.172160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.841444</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.153430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.838178</td>\n",
       "      <td>-0.172160</td>\n",
       "      <td>-0.153430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>dewPoint</td>\n",
       "      <td>0.796485</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.158787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>precipProbability</td>\n",
       "      <td>precipTypeIsRain</td>\n",
       "      <td>0.789718</td>\n",
       "      <td>0.010231</td>\n",
       "      <td>0.007165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.788791</td>\n",
       "      <td>-0.158787</td>\n",
       "      <td>-0.172160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.676692</td>\n",
       "      <td>-0.172160</td>\n",
       "      <td>-0.187925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>NDVI</td>\n",
       "      <td>0.666803</td>\n",
       "      <td>-0.165742</td>\n",
       "      <td>-0.187925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>precipIntensity</td>\n",
       "      <td>precipProbability</td>\n",
       "      <td>0.651221</td>\n",
       "      <td>-0.015459</td>\n",
       "      <td>0.010231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      col1                    col2      corr  \\\n",
       "2   apparentTemperatureMax          temperatureMax  0.993237   \n",
       "8   apparentTemperatureMin          temperatureMin  0.992886   \n",
       "18          temperatureMax      temperatureAverage  0.962823   \n",
       "5   apparentTemperatureMax      temperatureAverage  0.960737   \n",
       "9   apparentTemperatureMin      temperatureAverage  0.954348   \n",
       "19          temperatureMin      temperatureAverage  0.954346   \n",
       "11                dewPoint          temperatureMin  0.951772   \n",
       "6   apparentTemperatureMin                dewPoint  0.949988   \n",
       "12                dewPoint      temperatureAverage  0.903411   \n",
       "13         precipIntensity      precipIntensityMax  0.863038   \n",
       "0   apparentTemperatureMax  apparentTemperatureMin  0.849104   \n",
       "7   apparentTemperatureMin          temperatureMax  0.844614   \n",
       "3   apparentTemperatureMax          temperatureMin  0.841444   \n",
       "16          temperatureMax          temperatureMin  0.838178   \n",
       "1   apparentTemperatureMax                dewPoint  0.796485   \n",
       "15       precipProbability        precipTypeIsRain  0.789718   \n",
       "10                dewPoint          temperatureMax  0.788791   \n",
       "17          temperatureMax                    NDVI  0.676692   \n",
       "4   apparentTemperatureMax                    NDVI  0.666803   \n",
       "14         precipIntensity       precipProbability  0.651221   \n",
       "\n",
       "    corr col1 with target  corr col2 with target  \n",
       "2               -0.165742              -0.172160  \n",
       "8               -0.146381              -0.153430  \n",
       "18              -0.172160              -0.170282  \n",
       "5               -0.165742              -0.170282  \n",
       "9               -0.146381              -0.170282  \n",
       "19              -0.153430              -0.170282  \n",
       "11              -0.158787              -0.153430  \n",
       "6               -0.146381              -0.158787  \n",
       "12              -0.158787              -0.170282  \n",
       "13              -0.015459              -0.020094  \n",
       "0               -0.165742              -0.146381  \n",
       "7               -0.146381              -0.172160  \n",
       "3               -0.165742              -0.153430  \n",
       "16              -0.172160              -0.153430  \n",
       "1               -0.165742              -0.158787  \n",
       "15               0.010231               0.007165  \n",
       "10              -0.158787              -0.172160  \n",
       "17              -0.172160              -0.187925  \n",
       "4               -0.165742              -0.187925  \n",
       "14              -0.015459               0.010231  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newDF.sort_values(by='corr', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few features that show very strong correlation. For some that is to be expected (apparent temperature vs. temperature, precip intensity vs. max precip intensity, etc).\n",
    "\n",
    "The 'apparent' temperature values are slightly less correlated with the target variable ('Yield'). I will discard those from further analysis. While 'dew point' and 'temperature min' are highly correlated, thier correlation with 'Yield' has opposite sign (even though the linear correlation itself is weak). I will keep both for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Drop features/columns'></a>\n",
    "## Drop features/columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am dropping a number of features, because they either hold no predictive power (precipTypeIsOther), or are essentially very similar to and (extremely) highly correlated with other features (e.g., apparent temperature features)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- precipTypeIsOther\n",
    "- apparentTemperatureMin\n",
    "- apparentTemperatureMax\n",
    "- precipIntensityMax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2013.drop('precipTypeIsOther', axis=1, inplace=True)\n",
    "df_2013.drop('apparentTemperatureMin', axis=1, inplace=True)\n",
    "df_2013.drop('apparentTemperatureMax', axis=1, inplace=True)\n",
    "df_2013.drop('precipIntensityMax', axis=1, inplace=True)\n",
    "#\n",
    "df_2014.drop('precipTypeIsOther', axis=1, inplace=True)\n",
    "df_2014.drop('apparentTemperatureMin', axis=1, inplace=True)\n",
    "df_2014.drop('apparentTemperatureMax', axis=1, inplace=True)\n",
    "df_2014.drop('precipIntensityMax', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Save cleaned data to disk'></a>\n",
    "## Save cleaned data to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_2013.to_pickle(os.path.join('data','df_2013_clean.df'))\n",
    "df_2014.to_pickle(os.path.join('data','df_2014_clean.df'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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